Reviewerly/exHarmony huggingface dataset:
Using Vectorized Execution to Improve SQL Query Performance on Spark

MapReduce-based SQL processing frameworks, such as Hive and Spark SQL, are widely used to support big data analytics. Currently these systems mainly adopt the record-at-a-time execution model, which is less efficient in terms of CPU utilization. In contrast, vectorized execution is able to make better use of CPU cache by bulk processing a record batch at a time. However, simply applying vectorized execution to MapReduce-based frameworks results in low efficient vectorized shuffle. Moreover, existing vectorized execution donot make full use of CPU cache for complex operators (e.g. Sort and Aggregation). In this paper, we present VEE, a thorough vectorized execution engine designed for SQL query processing on Spark. First, VEE designs compact in-memory data layout and serialization-aware assembling for vectorized shuffle to expedites shuffle execution, since they reduce shuffle data footprint and related computations. Secondly, VEE applies in-memory record batch rearrangement for Sort and Aggregation to greatly reduce random memory access and increase query performance. Thirdly, VEE carefully designs operator-aware batch length when handling different operators, which makes better utilization of CPU cache and increases query performance. We conduct extensive performance evaluations. The experiment results show that the performance speedup of VEE against Spark is up to 72.7% and 25.0% on average for OLAP workloads (TPC-H). The vectorized execution technologies in VEE are also applicable to other MapReduce-based data analytic frameworks to improve their query performance.
The Role of Blockchain Technology and Its Usage in Various Sectors in the Modern Age

A decade earlier, the basic guiding theory of the blockchain was implemented. It took a few years for the technology to be widely recognized outside the computer science sector in industry and academic communities. Since then, several scientific institutions have taken up the topic. Through this chapter, the authors focus not only on the working mechanism of the technology but also towards the use cases in varied industries. Blockchain implementations are an effective way to actively move business expertise to study goals that support both technical growth and testing through analysis, design, and research approach.
Interaction-Based Document Matching for Implicit Search Result Diversification

To satisfy different intents behind the queries issued by users, the search engines need to re-rank the search result documents for diversification. Most of previous approaches of search result diversification use pre-trained embeddings to represent the candidate documents. These representation-based approaches lose fine-grained matching signals. In this paper, we propose a new supervised framework leveraging interaction-based neural matching signals for implicit search result diversification. Compared with previous works, our proposed framework can capture and aggregate fine-grained matching signals between each candidate document and selected document sequences, and improve the performance of implicit search result diversification. Experimental results show that our proposed framework can outperform previous state-of-the-art implicit and explicit diversification approaches significantly, and even slightly outperforms ensemble diversification approaches. Besides, with our proposed strategies the online ranking latency of our framework is moderate and affordable.
Scale Input Adapted Attention for Image Denoising Using a Densely Connected U-Net: SADE-Net

In this work, we address the problem of image denoising using deep neural networks. Recent developments in convolutional neural networks provide a very potent alternative for image restoration applications and in particular for image denoising. A particularly popular deep network structure for image processing are the auto-encoders which include the U-Net as an important example. U-Nets contract and expand feature maps repeatedly, which leads to extraction of multi scale information as well as an increase in the effective receptive field when compared to conventional convolutional nets. In this paper, we propose the integration of a multi scale channel attention module through a U-Net structure as a novelty for the image denoising problem. The introduced network structure also utilizes multi scale inputs in the various substages of the encoder module in a novel manner. Simulation results demonstrate competitive and mostly superior performance when compared to some state of the art deep learning based image denoising methodologies. Qualitative results also indicate that the developed deep network framework has powerful detail preserving capability.
Graph Technologies for User Modeling and Recommendation: Introduction to the Special Issue - Part 1

introduction Share on Graph Technologies for User Modeling and Recommendation: Introduction to the Special Issue - Part 1 Authors: Xiangnan He University of Science and Technology of China, He Fei, China University of Science and Technology of China, He Fei, ChinaSearch about this author , Zhaochun Ren Shandong University, Qingdao, China Shandong University, Qingdao, ChinaSearch about this author , Emine Yilmaz University College London, United Kingdom University College London, United KingdomSearch about this author , Marc Najork Google Research, Mountain View, CA, United States Google Research, Mountain View, CA, United StatesSearch about this author , Tat-Seng Chua National University of Singapore, Republic of Singapore, Singapore National University of Singapore, Republic of Singapore, SingaporeSearch about this author Authors Info & Claims ACM Transactions on Information SystemsVolume 40Issue 2April 2022 Article No.: 21pp 1–5https://doi.org/10.1145/3477596Online:27 September 2021Publication History 0citation282DownloadsMetricsTotal Citations0Total Downloads282Last 12 Months282Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Luring Transferable Adversarial Perturbations for Deep Neural Networks

The growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffensive or make the model more robust against them. In this paper, we pave the way towards a new approach to improve the robustness of a model against black-box transfer attacks. A removable additional neural network is included in the target model, and is designed to induce the luring effect, which tricks the adversary into choosing false directions to fool the target model. Training the additional model is achieved thanks to a loss function acting on the logits sequence order. Our deception-based method only needs to have access to the predictions of the target model and does not require a labeled data set. We explain the luring effect thanks to the notion of robust and non-robust useful features and perform experiments on MNIST, SVHN and CIFAR10 to characterize and evaluate this phenomenon. Additionally, we scale the luring effect to ImageNet, experiment practical use of it and discuss its complementarity with other defense schemes.
Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge

Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy rate despite its advantage of fast procession. In this paper, we model domain knowledge (i.e., background knowledge or side information), respecting some applications as must-link and cannot-link sets, for the sake of collaborating with k-means for better accuracy. We first propose an algorithm for constrained k-means, considering only must-links. The key idea is to consider a set of data points constrained by the must-links as a single data point with a weight equal to the weight sum of the constrained points. Then, for clustering the data points set with cannot-link, we employ minimum-weight matching to assign the data points to the existing clusters. At last, we carried out a numerical simulation to evaluate the proposed algorithms against the UCI datasets, demonstrating that our method outperforms the previous algorithms for constrained k-means as well as the traditional k-means regarding the clustering accuracy rate although with a slightly compromised practical runtime.
A new ASIC implementation of an advanced encryption standard (AES) crypto-hardware accelerator

Single-chip hardware implementation of Advanced Encryption Standard (AES) offers a low-power and low-area design that is suitable for portable devices. It is widely applicable for numerous encryption needs such as in Bluetooth controller, wireless communication and secure Internet transactions. This paper proposes a new full-custom compact 8-bit data-path architecture core for a single-chip VLSI AES crypto-hardware accelerator. In order to optimize chip-area, power and performance, novel circuit-level techniques, logic minimization, resource sharing and low supply-voltage has been employed. The proposed design is implemented in 130 nm CMOS process and supports both encryption and decryption in Electronic-Codebook-Mode (EBC) using 128-bit keys. Novel S-box/InvS-box, MixColumn/InvMixColumn and ShiftRow/InvShiftRow using low-power Exclusive-OR (XOR) gate is employed to minimize the power consumption. This design utilized 3120 gate-equivalents (GE), including an on-the-fly key scheduling unit with an active chip-area of 640μm × 325 μm (0.208 sq. mm) excluding the bonding pads. It has a power consumption of 4.23 μW/MHz and a throughput of 0.05 Gbit/s (at 100 MHz clock). The proposed AES design thus achieved low-power dissipation, higher throughput with a compact chip-size (silicon-area) compared to other recent implementations.
Skill Transfer of Traditional Pottery Techniques: Exploring the Effects of Metacognition Through Gaze Movement

The aim of this paper is to explore ways of transferring the skills of pottery making. By recording and replaying the process of using the potter’s wheel, we will be able to reexamine the history of where we are looking. We believe that one’s awareness will have both an important meaning and an effect on the skill acquisition. In fact, some insights for the participants were discovered such as reflection in ones’ own interest through the visualization. We also attempted to explain the results using a cognitive model, concluding with the significance of developing a system to support metacognition.
COVID-19 recommender system based on an annotated multilingual corpus

Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)-related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming information regarding this disease. A solution to this problem requires the development of text mining pipelines; the efficiency of which strongly depends on the availability of curated corpora. However, there is a lack of COVID-19-related corpora, even more, if considering other languages besides English. This project's main contribution was the annotation of a multilingual parallel corpus and the generation of a recommendation dataset (EN-PT and EN-ES) regarding relevant entities, their relations, and recommendation, providing this resource to the community to improve the text mining research on COVID-19-related literature. This work was developed during the 7th Biomedical Linked Annotation Hackathon (BLAH7).
Computer Vision Based Vulnerable Road Users Hand Signal Recognition

With development of computer vision (CV) and Artificial Intelligent (AI) the domains like object detection, object tracking their development from 2D to 3D tracking and human pose estimation have gained a lot of popularity and demand in the field of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). Basically, these systems are designed with an objective to increase the safety and avoid road accidents while driving. These systems develop the ability of car driver to react to various types of hazards and dangers on the road. If it is possible to know the intentions of Vulnerable Road Users (VRUs) like pedestrian, cyclist or motorbike riders then the probability of road accidents can be reduced to great extents. The ADAS systems are able to warn the car driver through automatic systems and thus provide high safety. This paper proposes Convolutional neural Networks (CNN) based VRUs hand signal recognition system based on Region Multi-Person Pose Estimation (RMPE) [1] framework for human pose estimation to know the hand signals given by the bicyclist, scooter, e-scooter and motorbike riders to warn the car driver. The VRUs are well aware of the traffic rules and regulations and also follow them. Based on the signal given by bicyclist the ego-vehicle can stop or reduce its speed. The focus of this novel algorithm proposed in this paper is that it is capable of recognizing all the six hand signals of bicyclists left, right, stop, give way, slow down and road hazard which are not addressed before in any previous works. Also, this algorithm can recognize arm signals of scooter, e-scooter riders and motorbike riders efficiently. The dataset used for testing is the self-captured dataset for VRUs Arm Signal Recognition and to generalize it Cyclist Arm Signal Recognition (CASR) [2] dataset is also used. The proposed work provides state-of-the-art (SOTA) results for VRUs hand signal recognition and frame rate of 10 FPS and 92% acuuracy.
A Summary of the Latest Research on Knowledge Graph Technology

In recent years, with the in-depth development of cognitive intelligence technology, knowledge graph and its related technologies have also made breakthroughs, specifically reflected in the construction of knowledge graph, reasoning and computing technology. On the basis of a comprehensive description of the definition of knowledge graph and the discussion of the development history of knowledge graph technology, this paper summarizes their current scientific research progress around the two major technologies of knowledge representation learning and knowledge extraction in knowledge graph. This paper discriminates the advantages and disadvantages of the two technologies, and points out the direction for the follow-up research and improvement of the technology. Finally, the paper makes a summary and prospect of the future research direction of knowledge graph technology.
New convolutional neural network models for efficient object recognition with humanoid robots

Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is still a challenging problem at different locations and different object positions in real time. The current paper presents four novel models with small structure, based on Convolutional Neural Networks (CNNs) for object recognition with humanoid robots. In the proposed models, a few combinations of convolutions are used to recognize the class labels. The MNIST and CIFAR-10 benchmark datasets are first tested on our models. The performance of the proposed models is shown by comparisons to that of the best state-of-the-art models. The models are then applied on the Robotis-Op3 humanoid robot to recognize the objects of different shapes. The results of the models are compared to those of the models, such as VGG-16 and Residual Network-20 (ResNet-20), in terms of training and validation accuracy and loss, parameter number and training time. The experimental results show that the proposed model exhibits high accurate recognition by the lower parameter number and smaller training time than complex models. Consequently, the proposed models can be considered promising powerful models for object recognition with humanoid robots.
Zero Knowledge Contingent Payments for Trained Neural Networks

Nowadays, neural networks have been widely used in many machine learning tasks. In practice, one might not have enough expertise to fine-tune a neural network model; therefore, it becomes increasingly popular to outsource the model training process to a machine learning expert. This activity brings out the needs of fair model exchange: if the seller sends the model first, the buyer might refuse to pay; if the buyer pays first, the seller might refuse to send the model or send an inferior model. In this work, we aim to address this problem so that neither the buyer nor the seller can deceive the other. We start from Zero Knowledge Contingent Payment (ZKCP), which is used for fair exchange of digital goods and payment over blockchain, and extend it to Zero Knowledge Contingent Model Payment (ZKCMP). We then instantiate our ZKCMP with two state-of-the-art NIZK proofs: zk-SNARKs and Libra. We also propose a random sampling technique to improve the efficiency of zk-SNARKs. We extensively conduct experiments to demonstrate the practicality of our proposal.
Research on Target Tracking Algorithm Based on Correlation Filtering

Abstract Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new progress has been made in this field, and it has been widely used in automatic driving,video surveillance, human-computer interaction, national defense security and other fields. Mainly summarize the research status in this field. First of all, the main components and research difficulties of current target tracking are introduced. Secondly, the algorithm of target tracking based on correlation filtering is introduced, and the development of the classical algorithm model and its existing problems are analyzed in detail. Finally, some views on the future research direction and development trend of related filtering algorithms are given.
Why Mixup Improves the Model Performance

Machine learning techniques are used in a wide range of domains. However, machine learning models often suffer from the problem of over-fitting. Many data augmentation methods have been proposed to tackle such a problem, and one of them is called mixup. Mixup is a recently proposed regularization procedure, which linearly interpolates a random pair of training examples. This regularization method works very well experimentally, but its theoretical guarantee is not adequately discussed. In this study, we aim to discover why mixup works well from the aspect of the statistical learning theory.
Scheduling Challenges for Variable Capacity Resources

Datacenter scheduling research often assumes resources as a constant quantity, but increasingly external factors shape capacity dynamically, and beyond the control of an operator. Based on emerging examples, we define a new, open research challenge: the variable capacity resource scheduling problem. The objective here is effective resource utilization despite sudden, perhaps large, changes in the available resources.We define the problem, key dimensions of resource capacity variation, and give specific examples that arise from the natural world (carbon-content, power price, datacenter cooling, and more). Key dimensions of the resource capacity variation include dynamic range, frequency, and structure. With these dimensions, an empirical trace can be characterized, abstracting it from the many possible important real-world generators of variation.Resource capacity variation can arise from many causes including weather, market prices, renewable energy, carbon emission targets, and internal dynamic power management constraints. We give examples of three different sources of variable capacity.Finally, we show variable resource capacity presents new scheduling challenges. We show how variation can cause significant performance degradation in existing schedulers, with up to 60% goodput reduction. Further, initial results also show intelligent scheduling techniques can be helpful. These insights show the promise and opportunity for future scheduling studies on resource volatility.
Motivating Players to Perform an Optional Strenuous Activity in a Virtual Reality Exergame Using Virtual Performance Augmentation

Research on VR Exergaming is mostly conducted with participants who are not presented a choice whether or not to play the exergame and thus to perform a certain strenuous activity. Whether players would engage in such activity if it were optional and how they could be motivated to do so is mostly neglected. Therefore, we have developed a novel controller and implemented a VR exergame that utilizes Virtual Performance Augmentation (VPA) to motivate players to engage in an optional strenuous activity. The motivational impact of three different conditions (VPA vs. Points vs. No Motivator) was evaluated in a study with 47 participants. Our results suggest that using VPA can significantly increase the time the players engage in the strenuous activity enriched by VPA, while in contrast to our hypotheses, no significant differences for the players' enjoyment and perceived exertion between the conditions were found. We discuss our findings in the context of motivation, exertion, and the resulting implications for further VR exergames research.
Cross-chain Workflow Model Based on Trusted Relay

The development of blockchain technology has led to the emergence of a large number of blockchain systems for different domains. These blockchain systems present the problem of data isolation. Chain interoperability technology, that is, cross-chain technology focuses on how to achieve value transfer and data interaction among different blockchains. However, the structural difference among the architectures of existing mainstream cross-chain solutions has brought three challenges to the cross-chain technology: the feasibility of cross-chain transactions, the structuring and standardization of cross-chain processes and the provenance of cross-chain processes.
Performance Enhancement of Solar Panel Using Dual Axis Solar Tracker

The design, development and assembly of microcontroller based Dual Axis Solar Tracker to enhance the performance of an immobile Solar Photo Voltaic (SPV) panel is presented in this paper. Four Light Dependent Resistors (LDRs) act as sensors to determine the intensity of sunbeams and communicate the same to Arduino UNO’s microcontroller. Depending on the command given by Arduino Uno’s microcontroller, the solar panel is being tilted perpendicular to the incident sun’s rays by using two servomotors. Thus, the developed prototype ensures that the efficiency of photovoltaic panel is increased in comparison to static solar photovoltaic panel.
Information-educational environment of the university as a quality of engineering education improvement factor

The quality of education is often the subject of discussion, as it largely depends on the equipment for the educational process and the technical means used in it. This article presents the formation of an effective educational environment of the university as one of the main quality of engineering education improvement factors. The composition and purpose of the educational environment are listed, the main components are considered, and examples of individual opportunities in the functioning of electronic resources in relation to the key elements of the modern educational technologies used in higher education institutions. The availability and functioning of the electronic educational environment became particularly relevant in 2021, in the context of a global pandemic.