Reviewerly/exHarmony huggingface dataset:
Robust online visual tracking via stable and adaptive memories

Visual tracking is a popular research topic in computer vision. In this paper, we propose a novel tracking framework which leverages stable and adaptive memories of target appearance for robust tracking where the target undergoes significant appearance change as well as background clutter. First, we define a stable-adaptive memory network which exploits the embedding of the target patch in the first video frame, named as "reliable memory", as well as the embeddings of patches collected online that are referred as "adaptive memories". Through the fusion of these two types of memories, a good balance between stability and plasticity can be made. During tracking, the network searches the candidate which has the highest similarity with the memorized patterns as the tracking result. Second, we train an online detector to re-detect the target in case of tracking failure and update the memory network. By virtue of the proposed mechanism, our tracker can handle the drift problem well and is able to track the object in challenging situations robustly. Experimental results on challenging benchmark video sequences show that the proposed tracking framework achieves state-of-the-art tracking performance with high accuracy and robustness.
Learning Plans by Acquiring Grounded Linguistic Meanings from Corrections

We motivate and describe a novel task which is modelled on interactions between apprentices and expert teachers. In the task the agent must learn to build towers which are constrained by rules. Whenever the agent performs an action which violates a rule the teacher provides verbal corrective feedback (e.g. No, put red blocks on blue blocks'') and answers the learner's clarification questions. The agent must learn to build rule compliant towers from these corrections and the context in which they were given. The agent starts out unaware of the constraints as well as the domain concepts in which the constraints are expressed. Therefore an agent that takes advantage of the linguistic evidence must learn the denotations of neologisms and adapt its conceptualisation of the planning domain to incorporate those denotations. We show that an agent which does utilise linguistic evidence outperforms a strong baseline which does not.
Judging style: The case of Bush versus Gore

Abstract The judgments by members of the US Supreme Court in the 2000 case of Bush versus Gore remain controversial to the present. We use text mining and machine learning methods to compare the word usage patterns of Supreme Court Justices in order to explore the likely authorship of both the anonymous 5-4 per curiam decision in this case and the concurrence that is attributed to Chief Justice Rehnquist, with Scalia and Thomas joining. An analysis of high and medium frequency words suggests that Justice Kennedy was likely the main contributor to the per curiam decision. A similar analysis of the concurrence, however, suggests that Justice Scalia may have played a more central role than the document’s purported author, Justice Rehnquist. Our analysis indicates that while Chief Justice Rehnquist was likely to have been the crafter of the document, much of the more forceful language of the concurrence resonates more clearly with a vocabulary that is indicative of Justice Scalia.
Open Reading and Free Choice Permission: A Perspective in Substructural Logics

This paper proposes a new solution to the well-known Free Choice Permission Paradoxes (Barker 2010; Hansson 2013; Xin and Dong 2014), combining ideas from substructural logics and non-monotonic reasoning. Free choice permission is intuitively understood as “if it is permitted to do $$\alpha $$ or $$\beta $$ then it is permitted to do $$\alpha $$ and it is permitted to do $$\beta $$ .” Yet, one of its logically equivalent forms allows the following inference which seems unacceptable: if it is permitted to order a vegetarian lunch then it is permitted to order a vegetarian lunch and not pay for it (Hansson 2013). The challenge for a logic of free choice permission is to exclude such counterintuitive consequences while not giving up too much deductive power. We suggest that the right way to do so is using a family of substructural logics augmented with a principle borrowed from non-monotonic reasoning. This follows up on a proposal made in Anglberger et al. (2014).
Privacy-preserving Data Mining in Industry

Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS / macOS, Google’s RAPPOR, LinkedIn Salary, and Microsoft’s differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Car Theft Reports: a Temporal Analysis from a Social Media Perspective

Complex human behaviors related to crime require multiple sources of information to understand them. Social Media is a place where people share opinions and news. This allows events in the physical world like crimes to be reflected on Social Media. In this paper we study crimes from the perspective of Social Media, specifically car theft and Twitter. We use data of car theft reports from Twitter and car insurance companies in Chile to perform a temporal analysis. We found that there is an increasing correlation in recent years between the number of car theft reports in Twitter and data collected from insurance companies. We performed yearly, monthly, daily and hourly analyses. Though Twitter is an unstructured source and very noisy, it allows you to estimate the volume of thefts that are reported by the insurers. We experimented with a Moving Average to predict the tendency in the number of car theft reported to insurances using Twitter data and found that one month is the best time window for prediction.
Application of graph theory in transportation linkage in logistics management and its computer aided model design

Path planning is an important part of logistics management and an important part of intelligent transportation system. The shortest path problem in graph theory is the theoretical basis of path planning. On this basis, this paper studies the application of graph theory in logistics management and transportation linkage and its computer-aided model design. In order to give full play to the advantages of the optimized road network model, this paper adopts the matching path planning algorithm and control strategy. The optimized road network model puts forward a partition method based on data quantity and connectivity principle and realizes the equalization of partition data quantity. Compared with the general road network model, the established road network partition is more reasonable and more in line with the actual requirements of the path planning algorithm. Through the research in this paper, it provides a reasonable and effective solution for practical application, which is innovative and practical.
Applied Digital Threat Modeling: It Works

Threat modeling, a structured process for identifying risks and developing mitigation strategies, has never been systematically evaluated in a real environment. Our case study at the New York City Cyber Command-the primary digital defense organization for the most populous city in the United States-found tangible benefits.
Investigating fluid-rock interaction at the hand-specimen scale via ITRAX

Accessing geochemical compositions on the hand-specimen scale provides important clues towards understanding fluid-rock interaction in mineralization. In this study, we used the ITRAX core scanner to detect the geochemical composition of a hand specimen obtained from Yangshan skarn-type Iron deposits in China. In order to investigate fine-scale nature of element immigration during the fluid rock interaction, the discrete wavelet leader transform combined with log-cumulants was utilized to calculate the multifractal spectrum which can describe the degree of the irregularity and heterogeneity of the geochemical distributions. The present results show that the iron, manganese, aluminum, europium, silicon and calcium are multifractally distributed with different singularity spectra. Among them iron and manganese hold narrower singularity spectrum than other elements, indicating that their distributions are more regular or homogeneous. By comparing the multifractal characteristics between the vein area and the wall-rock area, we show that the distributions of the strongly affected sections (near the vein area) would be more homogeneous than those of the weakly affected sections. This fact demonstrates that the fluid-rock interaction (isomorphism between Fe2+ and Mn2+) homogenized the nature stochastic distribution of original hydrothermal systems. The multifractal characteristics described by the first two log-cumulants provide a valid way to investigate the intensity of fluid-rock interactions through measuring the heterogeneity of geochemical distributions on the hand-specimen scale.
Team Arrangement Heuristic Algorithm (TAHA): Theory and application

In this research study a novel human inspired optimization algorithm namely Team Arrangement Heuristic Algorithm (TAHA) is proposed, based on the pyramidal structure of a company and also the activities of each member in the company. It is assumed that in a company three groups of members do activities, which are the CEO, directors and employees. The right arrangement of these members and also connection between them will lead the company to the best situation where, the best project will be handled by the company, with the best members and the project will be precisely finished at its dead line with a high quality. The performance of the proposed algorithm has been evaluated with popular unimodal and multimodal functions. Also CEC2005 benchmark functions are used as a challenging problems. Seven popular optimization algorithms namely, particle swarm optimization (PSO), cuckoo search (CS), fire fly algorithm (FA), flower pollination algorithm (FPA), krill herd (KH), grey wolf optimizer (GWO) and gravitation search algorithm (GSA) are used for the purpose of comparison. Two real case engineering problems, which are heat wheel optimization problem and horizontal axis tidal current turbine problem, are solved using TAHA and other mentioned algorithms. The results indicated that TAHA outperforms other algorithms in several cases and it has a great performance in solving complicated optimization problems.
CCMA: A Dynamical Concurrent-Connection Management Agent to Mitigate TCP Incast in Datacenters

While aggregator applications with many-to-one TCP traffic pattern prevail in datacenter networks, they often suffer from catastrophic goodput degradation due to their highly bursty traffic overwhelming a single bottleneck link, which is known as the TCP Incast issue. In this paper, we first derive three analytical results that mitigate Incast via managing connections, i.e., equalizing the in-flight packet amounts of concurrent connections, closing all the connections with higher timeout possibilities, and closing some of the connections with identical timeout possibilities. Afterward, we design an Incast mitigation application, i.e., concurrent-connection management agent (CCMA). Unlike previous Incast solutions, CCMA is an application-layer agent on the receiver, which manages concurrent TCP connections on behalf of aggregator applications. CCMA requires no modification to existing communication hardware or software. For multiple coexisting aggregators, CCMA sequentially schedules them to avoid traffic collision. For each aggregator, CCMA dynamically adjusts the advertised window sizes and the concurrency number of each aggregator following our analytical results to achieve low Incast probability and high goodput. The extensive real-world experiments and NS3 simulations show that CCMA maintains zero Incast probability while greatly improving the goodput of TCP by 7.8–13.5 times.
Multilevel Data Concealing Technique Using Steganography and Visual Cryptography

Steganography is a data hiding technique which uses images, audio or video as a cover medium. Cryptography has become an essential part of security. Image Steganography is one such way to hide secret messages in an image to reduce vulnerability to cryptanalysis. We overcome the drawbacks of using only textual steganography as it is easier to intercept and decipher. We encrypt the plaintext with a randomly generated key using XOR and One Time Pad (OTP) Algorithm and in turn embedding it into the Least Significant Bit (LSB) of the cover image. We embed the cipher text in LSB of the pixels of the cover image to form Stego image. To enhance and ensure security, we use visual cryptography along with image scrambling. Image scrambling is a technique in which the location of pixels is scrambled to provide extra protection to the Stego image. Visual cryptography is a method used to encrypt the visual information by breaking it into shares. Using both image scrambling and visual cryptography makes the system not only more secure but also difficult to decrypt. A decryption algorithm for the same is also constructed in this paper.
Automatically Learning a Human-Resource Ontology from Professional Social-Network Data

In this work, we build an ontology (automatically learned) in the domain of Human Ressources by using a simple, efficient and undemanding procedure. Our principal challenge is to tackle the problem of automatically grouping human-provided job titles into a hierarchy and by similarity (as they are presented in human-made HR ontologies). We use the Louvain algorithm, a greedy optimization method that, given a sufficient amount of data, interconnects domain-specific jobs that have more skills in common than jobs from different domains. In our case, we used publicly available profiles from LinkedIn (written in English by users in France). An automatic evaluation was performed and shows that the resulting ontology is similar in size and structure to ESCO (one of the most complete human-made ontology for HR). The whole procedure allows recruitment professionals to easily generate and update this ontology with virtually no human intervention.
Cost-efficient Deployment of Big Data Applications in Federated Cloud Systems

Deployment of big data applications requires lot of computing resources as well as bandwidth for data transfer. Federated cloud systems offer a cost-effective solution for such applications requiring storage, processing and computing at scale. However, this comes with the associated energy and bandwidth costs. Our work addresses the problem of minimizing the total operating cost in deploying big data applications in federated cloud systems, including the cost of electricity and bandwidth. We propose an algorithm which considers the communication footprint of the application to minimize the bandwidth cost. The algorithm additionally leverages the diversity in electricity prices across the data centers to minimize the cost of electricity consumption. Experimental results show that the proposed algorithm outperforms other baseline methods in minimizing the operating cost of the cloud provider and also shows the advantages of federation.
Ground Sports Strategy Formulation and Assistance Technology Develpoment: Player Data Acquisition from Drone Videos

Sports analytics aims to provide a competitive advantage to a team or individual in sports by analyzing a collection of relevant statistics. Better strategies in play can be formulated using the information. The core technology necessary for the analysis is the player detection and tracking. Due to limitation of fixed cameras, this work presents a court detection using markers, player detection and tracking using a drone. The method presented is based on perspective transformation, template matching and particle filters. In this work, we can visualize the movement trajectory of a player and extract the real world (on court) player position irrespective of drone height and position.
An Abnormal Phone Identification Model with Meta-learning Two-layer Framework Based on PCA Dimension Reduction

In the telecommunications industry, it is a critical and challenging problem that identify fraudulent calls in time. In the traditional abnormal phone identification method, there are generally cases where the initiative is weak and the recognition accuracy is low. In order to solve the problem of data sample imbalance and dirty data in the sample set, we use ensemble algorithms to improve the recognition accuracy of abnormal phones. Specially, we design a meta-learning two-layer framework (MTF) algorithm by integrating heterogeneous learners based on PCA dimension reduction. The experiment demonstrates that the MTF model has a great improvement in the abnormal phone identification compared with traditional classification method.
ICT-Based Science Education: Main Digital Resources and Characterisation

This chapter analyses the main digital resources and Information and Communication Technologies (ICT) and their effects on science education. Different studies were used to reflect on two aspects: (1) science teaching and learning through ICT tools and digital resources and (2) science teaching and learning through the use of digital laboratories. It is verified that in the literature in science education, there are different emphases, such as an emphasis on the use of software, the Internet, computer games, and virtual and remote laboratories, among others. Also, there are examples of the effective use of ICT; however, studies suggest that the essential component for ICT-based science teaching and learning is determined by the teacher's pedagogical approaches. In particular, there are few studies that report strategies and didactics for the use of ICT in science classes, and they place a greater emphasis on outcomes and resources rather than on the teaching process.
Dynamic Load Balancing Using Hybrid Approach

Load balancing in a cloud environment for handling multiple process of different size is an important issue. Many advanced technologies are incorporated in the processes-based resource allocation which enhances the system efficiency. The steps of allotting resources to process can be done by taking data which helps to analyze and make important decisions at runtime. This article focuses on the allocation of cloud resources where two models were developed, the first was TLBO (Teacher Learning Based Optimization), a genetic algorithm which finds the correct position for the process to execute. Here, some information used for analysis was total number of machines, memory, execution time, etc. So, the output of the TLBO process sequence was used as training input for the Error Back Propagation Neural Network for learning. This trained neural network improved the work job sequence quality. Training was done in such a way that all sets of features were utilized to pair with their process requirement and current position. For increasing the reliability of the work, an experiment was done on a real dataset. Results show that the proposed model has overcome various evaluation parameters on a different scale as compared to previous approaches adopted by researchers.
Strategic documents to define the role of the libraries

Currently, the creation of strategic documents and the implementation of the action plan to achieve the goals and objectives set out in such documents is widespread among large business companies, both manufacturing and mining sectors, the retail sector, as well as elevated to the rank of mandatory for corporations with state participation, Federal state unitary Enterprises and a number of similar organizations. Such requirements are imposed on a number of sectors of the economy, and the cultural sector, where there are two documents of strategic goal – settingthe Basis of the state cultural policy and the Strategy of the state cultural policy, has not escaped this. It is important that these strategic planning documents, which represent a whole body of normative legal acts defining the goals and directions of the country's development, are of significant legal significance, as they are approved by the Decrees of the President of the Russian Federation or by resolutions and orders of the Government of the Russian Federation, which, in particular, means that they are binding. The article is devoted to the analysis of approaches to the definition of the role of libraries presented in various strategic documents of the Federal level, approved in recent years, which in a General sense can be considered clearly formulated "state order" for the implementation of which the activities of public libraries of the country should be focused in the coming years and in the long term.
Faster algorithms for 1-mappability of a sequence

In the k-mappability problem, we are given a string x of length n and integers m and k, and we are asked to count, for each length-m factor y of x, the number of other factors of length m of x that are at Hamming distance at most k from y. We focus here on the version of the problem where k=1. There exists an algorithm to solve this problem for k=1 requiring time O(mnlog⁡n/log⁡log⁡n) using space O(n). Here we present two new algorithms that require worst-case time O(mn) and O(nlog⁡nlog⁡log⁡n), respectively, and space O(n), thus greatly improving the previous result. Moreover, we present another algorithm that requires average-case time and space O(n) for integer alphabets of size σ if m=Ω(logσ⁡n). Notably, we show that this algorithm is generalizable for arbitrary k, requiring average-case time O(kn) and space O(n) if m=Ω(klogσ⁡n), assuming that the letters are independent and uniformly distributed random variables. Finally, we provide an experimental evaluation of our average-case algorithm demonstrating its competitiveness to the state-of-the-art implementation.
Trade-off between Performance and Energy Management in Autonomic and Green Data Centers

In recent years, energy optimization of cloud data centers got important consideration since data centers in activity frequently expend huge energy rates. In fact, the expanding processing capacity of data centers and its complexity, increases impressively their energy consumption which prompts to important losses for companies. With the end goal to reduce the energy consumption of the system, researchers and cloud specialist have proposed techniques which experience the design of locations for data centers, and others adopted methods for the optimized management of resources in data centers. While other researchers, adopted the autonomic computing paradigm for having automated and adaptive cloud data center management. Most existing energy efficiency techniques focus on how to properly solve the tradeoff between performance and energy consumption in cloud computing infrastructure. In this paper, we focus on the energy consumption of the data centers and how this can be reduced in order to make the cloud computing greener. Likewise, we highlight the importance of integrating the autonomy aspect to the solution so as to avail the most optimum level of resources management with minimized server energy consumption.