Reviewerly/exHarmony huggingface dataset:
Less is More: Service Profit Maximization in Geo-Distributed Clouds

Nowadays cloud providers purchase a good deal of bandwidth from Internet service providers to satisfy the growing requests from corporate customers for the exclusive use of inter-datacenter bandwidth. For exclusive bandwidth services, neither maximizing the revenue nor minimizing the cost can bring the maximal profit to cloud providers. The diversity of bandwidth prices and the random arrival time of user requests further increase the difficulty in economically scheduling the services to meet user requests from cloud providers. In this article, we propose to help cloud providers maximize their service profits by properly selecting user requests to serve rather than satisfying them all. We formulate the problem of service profit maximization and prove its NP-hardness. To handle offline request submission, we propose a solution that maximizes the service profit by alternately maximizing the service revenue and minimizing the service cost. To maximize service profit under online request submission, we propose an online scheduling algorithm that carefully handles the risk of not being able to pay off the incremental service cost and makes scheduling decisions in real time. Our extensive evaluations demonstrate that our solutions can achieve more than 1.6x the service profits of existing solutions.
SLA+: Narrowing the Difference between Data Sets in Heterogenous Cross-Project Defection Prediction

Different from existing cross-project defection prediction(CPDP) problems which assume that there is a close relation between the source data sets and the target data sets, in the heterogenous cross-project defection prediction(HCPDP) problem, the target data sets can be totally different from the source data sets. In order to narrow the difference between source data sets and target data sets, we implemented our own algorithm SLA + based on the selective learning algorithm . We select one of the multiple sources that have the highest similarity to the target data set as the source data set, and select one or more of the other source data sets that are similar to both the target data set and the source data set as an intermediate domain. We set up a bridge between the target domain and the source domain through the intermediate domain , breaking the large distribution gap for transferring knowledge between the source domain and the target domain. Besides, we achieve the purpose of dimensionality reduction by mining the potential relationship between features. We have done experiments on open source data sets, and the data sets used are all heterogeneous. The experiments prove that our method achieves comparable results compared with state-of-the-art HCPDP in most cases.
A Reconfigurable DNN Training Accelerator on FPGA

In recent years, deep neural networks (DNNs) have been widely applied in various tasks, demonstrating outstanding performance. To further outspread in practical applications, the efficient hardware implementation of DNNs is becoming a critical issue. With the rise of online learning, training DNNs on resource-constrained platforms has attracted more attention most recently. In this paper, we propose an FPGA-based accelerator for efficient DNN training. First, a reconfigurable processing element is designed, which is flexible to support various computation patterns during training in a unified architecture. Second, a well optimized architecture is presented to perform the computation of batch normalization layers in different stages. Finally, a prevailing model (ResNet-20) for CIFAR-10 dataset is implemented on Xilinx VC706 platform with our framework. Experimental results show that our design achieves 421 GOPS and 43.18 GOPS/W in terms of throughput and energy efficiency, respectively. The comparison results illustrate that our accelerator significantly outperforms prior works.
Generalized Nets as Tools for Modelling of the Neural Networks

In a series of papers, Generalized Nets (GNs) are constructed representing the functioning and the results of the work of different types of Neural Networks (NNs). In the present research, we show that the functioning and the results of the work of a given NN can be represented by a GN from a very simple type, but the so constructed GN-model can be used for extension of the concept of NNs.
Trainable Kalman Filter Based on Recurrent Neural Network and its Application in Aviation Surveillance

Abstract The most commonly used algorithm for aviation surveillance is Kalman filter. The accuracy of Kalman filter is affected by the accuracy of its parameters. When the parameters may change with environment change, the accuracy of traditional linear Kalman filter will be affected, in severe cases, filtering divergence will occur. This paper proposes an aviation surveillance filtering model that treats Kalman filter as the kernel of Recurrent Neural Network, uses Back Propagation Neural Network to predict parameters of Kalman filter. Which let Kalman filter be trainable and have ability to estimate parameters dynamically. Moreover, actual radar measurement data is used for radar filtering experiment, and the experiment results show the feasibility of this model, and show that this model has better accuracy and adaptability than traditional Kalman filter.
An Improved Salp Swarm Algorithm Based on Spark for Feature Selection

Salp swarm algorithm (SSA) is a population-based optimization technique with excellent performance. However, due to the lack of inertial parameters, this algorithm lacks the ability to find the global search for potential solutions. In this paper, we present an improved SSA algorithm based on spark(Spark-BSSA), which takes advantages of the improved global search ability of BSSA and combines with the spark programming mode. Experimental results demonstrated that as the number of cluster nodes increases, the running time of the algorithm decreases. At the same time, the proposed method is evaluated using the real data sets and compared with the binary gentic algorithm (BGA), the binary particle swarm algorithm (BPSO) and the binary gravity search algorithm (BGSA). The method has also higher classification performance and better stability of classification accuracy. Through experimental data analysis, the ultimate goal is to solve the problems of premature convergence and optimality in the original algorithm.
A Novel Algorithm for Shot Boundary Detection and Segmentation of Broadcast News Videos

News videos are an important means of conveying information. But the number of news broadcasted each day in a particular channel is very large and considering the number of news channels, it is a huge amount of information conveyed each day. Sorting of news videos plays a very important role in such cases to create a well-categorized database of news videos. Broadcast news video shot segmentation is one of the vital parts of sorting. This paper presents a novel algorithm that can detect the shot boundaries of news videos efficiently and perform shot segmentation accordingly. The algorithm adapts itself according to the input news video and hence is news channel independent.
A query engine for zero-streaming cameras

Low-cost wireless cameras are growing rapidly. With the help of advanced machine learning models (e.g., CNNs), those videos exhibit high business and social values, e.g., for retailing planning [18], wildlife study [21], and traffic monitoring [19, 25]. However, with high compute need, traditional video analytics systems [14, 15, 26, 27] require all videos to be uploaded to a backend server, which stresses the scarce network bandwidth between cameras and servers.
Feasibility Analysis and Application of Reinforcement Learning Algorithm Based on Dynamic Parameter Adjustment

Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still follows the empirical attempts of traditional machine learning (supervised learning and unsupervised learning). This method ignores part of the information generated by agents exploring the environment contained in the updating of the reinforcement learning value function, which will affect the performance of the convergence and cumulative return of reinforcement learning. The reinforcement learning algorithm based on dynamic parameter adjustment is a new method for setting learning rate parameters of deep reinforcement learning. Based on the traditional method of setting parameters for reinforcement learning, this method analyzes the advantages of different learning rates at different stages of reinforcement learning and dynamically adjusts the learning rates in combination with the temporal-difference (TD) error values to achieve the advantages of different learning rates in different stages to improve the rationality of the algorithm in practical application. At the same time, by combining the Robbins–Monro approximation algorithm and deep reinforcement learning algorithm, it is proved that the algorithm of dynamic regulation learning rate can theoretically meet the convergence requirements of the intelligent control algorithm. In the experiment, the effect of this method is analyzed through the continuous control scenario in the standard experimental environment of ”Car-on-The-Hill” of reinforcement learning, and it is verified that the new method can achieve better results than the traditional reinforcement learning in practical application. According to the model characteristics of the deep reinforcement learning, a more suitable setting method for the learning rate of the deep reinforcement learning network proposed. At the same time, the feasibility of the method has been proved both in theory and in the application. Therefore, the method of setting the learning rate parameter is worthy of further development and research.
Entangled states as robust and re-usable carriers of information

Entangled states can be used as secure carriers of information much in the same way as carriers are used in classical communications. In such protocols, quantum states are uploaded to the carrier at one end and are downloaded from it in safe form at the other end, leaving the carrier intact and ready for reuse. Furthermore, protocols have been designed for performing quantum state sharing in this way. In this work, we study the robustness of these protocols against two of the most common sources of noise, namely de-phasing and depolarization and show that multiple uses of these carriers do not lead to accumulative errors, rather the error remains constant and under control.
Comparative Analysis of Classic Computer Vision Methods and Deep Convolutional Neural Networks for Floor Segmentation

In the paper, we analyze the problem of automatic room floor segmentation. For this purpose, we consider several classic computer vision algorithms as well as some of the deep convolutional neural network architectures. The segmentation results are illustrated and compared. An idea for combining two groups of methods is proposed. It is demonstrated that a proper fusion provides the best segmentation quality.
Cross-Language Generative Automatic Summarization Based on Attention Mechanism

Generative automatic summarization is a basic problem in natural language processing. We propose a cross-language generative automatic summarization model. Unlike the traditional methods that have to go through machine translation, our model can directly generate a text summary of another language from a text body in one language. We use the RNNLM(Recurrent Neural Network based Language Model) structure to pre-train word vectors to obtain semantic information in different languages. We combined the Soft Attention mechanism in the Seq2Seq model, using Chinese, Korean and English to build a parallel corpus to train the model, thereby, cross-language automatic summarization can be achieved without the help of machine translation technology. Experiments show that the improvement of our proposed model on ROUGE-1, ROUGE-2 and ROUGE-L indicators reached 6%, 2.46%, and 5.13%, respectively. The experimental effect is friendly.
Adaptable Conversational Machines

In recent years we have witnessed a surge in machine learning methods that provide machines with conversational abilities. Most notably, neural‐network‐based systems have set the state of the art for difficult tasks such as speech recognition, semantic understanding, dialogue management, language generation, and speech synthesis. Still, unlike for the ancient game of Go for instance, we are far from achieving human‐level performance in dialogue. The reasons for this are numerous. One property of human‐human dialogue that stands out is the infinite number of possibilities of expressing oneself during the conversation, even when the topic of the conversation is restricted. A typical solution to this problem was scaling‐up the data. The most prominent mantra in speech and language technology has been “There is no data like more data.” However, the researchers now are focused on building smarter algorithms — algorithms that can learn efficiently from just a few examples. This is an intrinsic property of human behavior: an average human sees during their lifetime a fraction of data that we nowadays present to machines. A human can even have an intuition about a solution before ever experiencing an example solution. The human‐inspired ability to adapt may just be one of the keys in pushing dialogue systems toward human performance. This article reviews advancements in dialogue systems research with a focus on the adaptation methods for dialogue modeling, and ventures to have a glance at the future of research on adaptable conversational machines.
Second Workshop on the Impact of Recommender Systems at ACM RecSys ’20

Recommender systems research is largely focused on the value such systems can create for users, e.g., by helping them finding items of interest in situations of information overload. However, there are various other ways in which recommender systems can create value and have an impact on individuals and organizations. The goal of the workshop is to serve as a platform where researchers discuss recent insights on how recommender systems affect individuals, user communities, or organizations. The workshop also aims at raising awareness regarding the importance of impact-oriented research.
Privacy-Preserving Graph Operations for Social Network Analysis

Nowadays, our daily life is surrounded by various social networks, and they play an important role for people to communicate with others. The social networks contain large amount of valuable information, that can be used for research and business purposes. As a result, social network analysis and data mining receive lots of research attentions in recent years. Graph structure is commonly used in social network analysis, since it is easy to convert the data in social networks into graph-structured data, and various graph algorithms can help to solve different computing problems. In this paper, we investigate performing graph operations in a privacy-preserving manner, which are widely used in social network analysis. We propose two protocols that allow two parties to jointly compute the intersection and union of their graphs. Our protocols utilize homomorphic encryption to prevent information leakage during the process, and we provide security proofs of the protocols in the semi-honest setting.
Design of Tire Damage Image Recognition System Based on Deep Learning

Abstract In view of the problems of low efficiency, prone to misjudgment, and complicated identification levels in the current market, the use of manual judgment methods for the cause of tire damage. A method of tire damage image recognition based on convolutional neural network algorithm is proposed, and an intelligent tire damage image recognition system is designed. Through image processing, feature extraction, classification and identification of the input tire damage image, the computer can automatically output the cause of the damage. The experimental results show the feasibility of this method and also improve the shortcomings of traditional manual judgment of the cause of tire damage.
Meta-Learning Approaches for Recovery Rate Prediction

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine the predictions from 20 candidate linear, nonlinear and rule-based algorithms and we exploit a data set of predictors including security-specific factors, macro-financial indicators and measures of economic uncertainty. We find that the most promising approach consists of models combinations trained on security-specific characteristics and a limited number of well-identified theoretically sound recovery rate determinants, including uncertainty measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in designing micro and macro-prudential policies.
Map-Based Particle Filter for Localization: Autonomous Vehicle

The main task in navigation of an autonomous vehicle is to have accurate and robust localization. There are variety of localization techniques in study using particle filter; for localization with or without using map information in the measurements. There is a lack of study in localization methods employing map data in particle filter. This paper summarizes the localization techniques that use map information in particle filter for estimation. The study of various literature in the particle filter for localization using map data shows that, accurate and robust localization can be achieved for an autonomous vehicle provided the map to be used in the measurements is constructed precisely with the necessary features like road curbs, edges, trajectories etc.
Assisted Caretaking System for Geriatric Home Care

This project is being developed to access elder dependent people, inspired by a geriatric care management@home. According to the project’s main assessment, three stages have been completed and now, in the fourth stage, we are implementing a bio-network with several sensors that assists the caregiver in applying the therapeutic plan. At this point, we have collected data regarding the level of dependency of the elderly, and the risk of development of pressure ulcers, so that we can adapt the sensors according to their need. For example, sensors installed in a bed can warn the caregiver that it is time to change the position of the elderly patient. As such, after testing a prototype in laboratory, we installed the system in two homes where we are currently collecting data.
Research on Improved Census Binocular Stereo Matching Algorithm

Aiming at the defects that the traditional Census algorithm uses a fixed window and a fixed threshold to cause the image to have discontinuous depths and low matching accuracy in weak texture regions, an improvement is proposed. The cost computation phase uses SAD-Census algorithm, and proposes a new type of adaptive window method. The gradient information is used to dynamically select the threshold value to realize the selection of the window, and the Census cost computation is optimized. Consider the whole picture, Complete cost aggregation at multiple scales based on minimum spanning tree(MST); introduce left and right consistency detection methods to detect mismatched points in occluded areas, smooth the image through singular point filling and median filtering, and improve the overall accuracy of the improved algorithm. Using Middlebury dataset for testing, the experimental results show that the improved algorithm proposed in this paper has significantly improved matching accuracy and robustness compared with traditional algorithms, especially in areas with deep discontinuities and weak textures.
Using API-Embedding for API-Misuse Repair

Application Programming Interfaces (APIs) are a way to reuse existing functionalities of one application in another one. However, due to lacking knowledge on the correct usage of a particular API, developers sometimes commit misuses, causing unintended or faulty behavior. To detect and eventually repair such misuses automatically, inferring API usage patterns from real-world code is the state-of-the-art. A contradiction to an identified usage pattern denotes a misuse, while applying the pattern fixes the respective misuse. The success of this process heavily depends on the quality of the usage patterns and on the code from which these are inferred. Thus, a lack of code demonstrating the correct usage makes it impossible to detect and fix a misuse. In this paper, we discuss the potential of using machine-learning vector embeddings to improve automatic program repair and to extend it towards cross-API and cross-language repair. We illustrate our ideas using one particular technique for API-embedding (i.e., API2Vec) and describe the arising possibilities and challenges.
A matrix based list decoding algorithm for linear codes over integer residue rings

In this paper we address the problem of list decoding of linear codes over an integer residue ring Zq, where q is a power of a prime p. The proposed procedure exploits a particular matrix representation of the linear code over Zpr, called the standard form, and the p-adic expansion of the to-be-decoded vector. In particular, we focus on the erasure channel in which the location of the errors is known. This problem then boils down to solving a system of linear equations with coefficients in Zpr. From the parity-check matrix representations of the code we recursively select certain equations that a codeword must satisfy and have coefficients only in the field pr−1Zpr. This yields a step by step procedure obtaining a list of the closest codewords to a given received vector with some of its coordinates erased. We show that such an algorithm actually computes all possible erased coordinates, that is, the provided list is minimal.
GeoVisual analytics for understanding the distribution of speeding patterns on arterial roads: assessing the traffic safety of vulnerable road users

Arterial roads have operational significance and play a substantial role in the mobility of modern society. They make up the majority of road network in urban and rural areas and allow high-speed movement despite traffic-controlling elements. In densely populated areas where the presence of Vulnerable Road Users (VRUs) is high, high-speed movement is problematic, and speed calming measures are needed to improve traffic safety, since many VRUs do crossroads, regardless of the road network regulations. These aspects have been researched in the traffic domain in a small scale, and not much has been investigated from a visualisation perspective. To provide comprehensive insights on the movement characteristics of arterial roads, we propose a GeoVisual Analytics (GVA) approach. GVA techniques are suitable solutions to display and extract knowledge from large amounts of Floating Car Data (FCD) collected through on-board devices of vehicles. By cross-sector collaboration between cartographic and traffic experts, five arterial road segments in Aalborg City were selected to answer where and when in particular VRUs do cross streets by ignoring traffic rules. Based on clusters of large unexplainable deviations from driving speed in FCD, the results uncovered meaningful patterns from complex traffic movements. They also allowed for the provision of some recommendations that are critical for traffic safety in urban areas.