Reviewerly/exHarmony huggingface dataset:
Survey Results on Why CS Faculty Adopt New Teaching Practices

In a previous paper, we explored results from the first stage of a two-part research project designed to uncover what influences computer science (CS) faculty to adopt new teaching practices. In the first phase, we conducted interviews, classroom observations, and focus groups with faculty to better understand the organizational, individual, and social factors that influence faculty adoption. Here we discuss findings from the second phase of the project, which uses survey data from 821 CS faculty at 595 institutions in the U.S. to investigate the prevalence of themes uncovered during the qualitative phase. Results show that faculty who tried an innovation were motivated primarily by concerns for students' learning and course experience, including their engagement and participation. Also important were the "fit" with existing practices and tools, and the logistics of implementing an innovation. Factors that reduced faculty willingness to try an innovation include a lack of time, logistical issues, and satisfaction with their current teaching practices. Faculty learned about the innovations they later adopted through presentations and workshops at conferences and at their home institutions, and through conversations with respected colleagues who teach in similar contexts. Implications for encouraging more widespread usage of teaching techniques that improve diversity and student learning are discussed.
Robust Visual Tracking via an Improved Background Aware Correlation Filter

Recently, there emerge many excellent algorithms in the field of visual object tracking. Especially, the background aware correlation filter (BACF) has received much attention, owing to its ability to cope with the boundary effect. However, in the related works, there exist two aspects of imperfections: 1) only histograms of oriented gradients (HOG) is extracted, through which the visual information of targets cannot be fully expressed; and 2) the scale estimation strategy is imperfect in terms of scale parameters, which makes it impossible to accurately track the targets with large-scale changes. To overcome the imperfections, an improved BACF method of robust visual object tracking is proposed to achieve the location of targets with higher accuracy in complex scenarios allowing scale variation, occlusion, rotation, illumination variation, and so on. Crucially, a feature fusion strategy based on HOG and color names is integrated to extract a powerful feature of targets, and a modified scale estimation strategy is designed to enhance the ability to track targets with large-scale changes. The effectiveness and robustness of the proposed method are demonstrated through evaluations on OTB2103 and OTB2015 benchmarks. Particularly, compared with other state-of-the-art correlation filter-based trackers and deep learning-based trackers, the proposed method is competitive in terms of accuracy and success rate.
A Taxonomy of Virtualization Security Issues in Cloud Computing Environments

Objectives: To identify the main challenges and security issues of virtualization in cloud computing environments.It reviews the alleviation techniques for improving the security of cloud virtualization systems.Methods/ Statistical Analysis: Virtualization is a fundamental technology for cloud computing, and for this reason, any cloud vulnerabilities and threats affect virtualization.In this study, the systematic literature review is performed to find out the vulnerabilities and risks of virtualization in cloud computing and to identify threats, and attacks result from those vulnerabilities.Furthermore, we discover and analyze the effective mitigation techniques that are used to protect, secure, and manage virtualization environments.Findings: Thirty vulnerabilities are identified, explained, and classified into six proposed classes.Furthermore, fifteen main virtualization threats and attacks are defined according to exploited vulnerabilities in a cloud environment.Application/Improvements: A set of common mitigation solutions are recognized and discovered to alleviate the virtualization security risks.These reviewed techniques are analyzed and evaluated according to five specified security criteria.
Outsourcing scheme of ABE encryption secure against malicious adversary

Integrated broadcast-broadband services allow viewers to simultaneously receive broadcast content over the airwaves and additional information related to the content over the Internet. This integration provides opportunities for new services to be tailored and offered to individual viewers. Viewing histories provide a rich variety of data for service providers to learn the preferences of individual viewers and fine-tune their offerings. Each person's viewing history, however, is privacy-sensitive data and may reveal information that the viewer does not want revealed. In this paper, we propose a system that allows viewers to specify a policy that they would like to be applied to their viewing history, when shared with service providers, by using attribute-based encryption (ABE). A ciphertext is associated with a policy, and it can be decrypted only by service providers who conform to the policy. To reduce the computations of the user terminal, we develop a system with provable security that allows the encryption to be outsourced to a cloud server, without the need to trust the cloud server. To the best of our knowledge, our construction gives the first outsourcing scheme of ABE encryption that is secure against a malicious cloud server. Although our solution is described for integrated broadcast-broadband services, the architecture and results could also be used for sharing viewing histories of OTT (Over-The-Top) services such as Netflix and location histories of mobile services. We implemented our scheme and showed that it significantly reduces the computation cost of a user terminal: about one third that of the Waters' ABE scheme.
Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations

Online news media sites are emerging as the primary source of news for a large number of users. Due to a large number of stories being published in these media sites, users usually rely on news recommendation systems to find important news. In this work, we focus on automatically recommending news stories to all users of such media websites, where the selection is not influenced by a particular user's news reading habit. When recommending news stories in such non-personalized manner, there are three basic metrics of interest—recency, importance (analogous to relevance in personalized recommendation) and diversity of the recommended news. Ideally, recommender systems should recommend the most important stories soon after they are published. However, the importance of a story only becomes evident as the story ages, thereby creating a tension between recency and importance. A systematic analysis of popular recommendation strategies in use today reveals that they lead to poor trade-offs between recency and importance in practice. So, in this paper, we propose a new recommendation strategy (called Highest Future-Impact) which attempts to optimize on both the axes. To implement our proposed strategy in practice, we propose two approaches to predict the future-impact of news stories, by using crowd-sourced popularity signals and by observing editorial selection in past news data. Finally, we propose approaches to inculcate diversity in recommended news which can maintain a balanced proportion of news from different news sections. Evaluations over real-world news datasets show that our implementations achieve good performance in recommending news stories.
Suppressed possibilistic c-means clustering algorithm

The possibilistic c-means (PCM) clustering algorithm always suffers from a coincident clustering problem since it relaxes the probabilistic constraint in the fuzzy c-means (FCM) clustering algorithm. In this paper, to overcome the shortcoming of the PCM, a novel suppressed possibilistic c-means (S-PCM) clustering algorithm by introducing a suppressed competitive learning strategy into the PCM so as to improve the between-cluster relationships is proposed. Specifically, in the updating process the new algorithm searches for the biggest typicality which is regarded as winner by a competitive mechanism. Then it suppresses the non-winner typicalities with a suppressed rate which is used to control the learning strength. Moreover, the parameter setting problems of the suppressed rate and the penalty parameter in the S-PCM are also discussed in detail. In addition, the suppressed competitive learning strategy is still introduced into the possibilistic Gustafson–Kessel (PGK) clustering algorithm and a novel suppressed possibilistic Gustafson–Kessel (S-PGK) clustering model is proposed, which is more applicable to the ellipsoidal data clustering. Finally, experiments on several synthetic and real datasets with noise injection demonstrate the effectiveness of the proposed algorithms.
Negotiating team formation using deep reinforcement learning

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.
Chronology and phenomenology of the 1982 and 2015 Wolf volcano eruptions, Galápagos Archipelago

The 1982 and 2015 eruptions were the first at Wolf volcano, Galápagos Archipelago, with eyewitness accounts and satellite imagery. Both eruptions were characterized by a rapid and intense initial phase, with multiple eruptive vents, leading to the formation of large ‘a‘ā lava fields and scarce pāhoehoe mostly associated with the waning phases. The 1982 eruption started on 28 August from an intra-caldera vent that produced high lava fountains, as well as a radial fissure on the SE flank. The whole eruption lasted for at least 9 days and generated ~52.5E + 6 m3 DRE (dense rock equivalent) of lava. The 2015 eruption started on 25 May from a circumferential fissure that produced high lava fountains and deposited reticulite scoria on the flanks of the volcano. For the first time since the onset of volcano monitoring in Galápagos, we report cryptotephra from the 2015 eruption reaching and depositing in mainland Ecuador, 1400 km away from the source. Lava from the 2015 circumferential vents covered ~18.5 km2 on the SE and E flanks. On 13 June 2015, the eruption switched to an intra-caldera vent that was active until 30 June, and produced lava flows that covered most of the caldera floor. The 2015 eruption lasted 36 days and produced ~87E + 6 m3 DRE of lava, making it the fourth largest eruption in Galápagos since the eruption of Sierra Negra in 1979. We use combined ground-based geophysical surveillance, remote sensing, eyewitness accounts, and detailed field work to constrain the eruptive dynamics of this remote volcano. Our approach allows quantification of eruption rates, which are critical for understanding volcanic systems and for hazard assessment. First order rheological calculations permit us to further constrain the eruption dynamics and emplacement of the lava fields.
Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development

The paper introduces the motivation for creating dedicated speech corpora of air traffic control communication, describes in detail the process of preparation of corpora for both automatic speech recognition and text-to-speech synthesis, presents an illustrative example of speech recognition system developed using the automatic speech recognition corpora and finally describes the technical aspects of the data and the distribution channel.
Integrating character networks for extracting narratives from multimodal data

This study aims to integrate diverse data within narrative multimedia (i.e., artworks containing stories and distributed through multimedia) into a unified character network (i.e., a social network between characters that appear in the story). By combining multiple data sources (e.g., the text, video, and audio), we attempted to enhance the accuracy and semantic richness of existing character networks that confine themselves to a particular data source. To merge various data, we propose story synchronization for (i) improving the accuracy of data extracted from the narrative multimedia and (ii) integrating the data into the unified character network. The story synchronization mainly consists of three steps: synchronizing (i) scenes, (ii) characters, and (iii) character networks. First, we synchronize dialogues in the text and audio, to discover speakers and time of dialogues. This enables us to segment the scene using time periods when dialogues (in the text and audio) and characters (in the video) do not commonly occur. Through the scene segmentation, we can discretize stories in the narrative work. By comparing the occurrence of dialogues and characters in each scene, we synchronize identities of the characters in the text and video (e.g., names and faces of characters). Thereby, we can more accurately estimate participants and time of a conversation between characters (i.e., a set of connected dialogues). Based on the conversation, the existing character networks are refined and integrated into the unified character network. In addition, we verified the efficacy of the proposed methods using movies in the real world, which are among the most accessible and popular narrative multimedia.
Cryptojacking injection: A paradigm shift to cryptocurrency-based web-centric internet attacks

Crypto-mining attacks have emerged as a new generation of web-based attacks which have seen cybercriminals eschew the infamous crypto ransomware. The watering hole attack vector has by far been the most widely employed attack methodology but it faces the task of luring the victim to the infected web resources. However, cryptojacking injection presents a paradigm shift to web-based crypto-mining attacks in that it eliminates the need for a pivotal third-party such as the exploitable web server. Thus, instead of attacking credit card and other private information of e-commerce users, attackers seek to maliciously abuse a victim's CPU to generate cryptocurrency. In this paper, we investigate and evaluate cryptojacking injection – a state-of-the-art web-centric attack vector in the crypto-mining attacks landscape. We formulate an attack model based on finite state machines which depicts the various breaches of confidentiality, integrity and availability in the web system as the attack progresses. We show how this new attack vector attacks some of the core components of e-commerce (URL, HTTP and HTML) to generate Monero crypto currency from benign web users. We evaluate our modeling approach with a series of experiments with two attack scenarios using different operating systems. Results show that the attack is indeed cross-platform and feasible on any operating system of a browser-capable device. We analyze the generated network traffic during the attack and draw features such as URLs and the parsed files, the associated cryptographic hashes, and the IP addresses of the crypto-mining domains. These, together with host-based features such as exhaustive CPU usage can be used as indicators of compromise and subsequently act as feed into intrusion detection systems.
Towards Enabling Feedback on Rhetorical Structure with Neural Sequence Models

Analysis of student writing, both for assessment and for enabling feedback have been of interest to the field of learning analytics. While much progress can be made through detection of local cues in writing, structured prediction approaches offer capabilities that are particularly well tailored to the needs of models aiming to offer substantive feedback on rhetorical structure. We thus cast the analysis of rhetorical structure in academic writing as a structured prediction task in which we employ models that leverage both local and global cues in writing. In particular, this paper presents a hierarchical neural architecture that performs this task. The evaluation demonstrates that the architecture achieves near-human performance while significantly surpassing state-of-the-art baselines. A multifaceted approach to model interpretation offers insights into the inner workings of the model.
Comprehension Factor Analysis

Massive Open Online Courses (MOOCs) often incorporate lecture-based learning along with lecture notes, textbooks, and videos to students. Moreover, MOOCs also incorporate practice activities and quizzes. Student learning in MOOCs can be tracked and improved using state-of-the-art student modeling. Currently, this means employing conventional student models that are constructed around Intelligent Tutoring Systems (ITS). Traditional ITS systems only utilize students performance interactions (quiz, problem-solving or practice activities). Therefore, text interactions are entirely ignored while modeling students performance in MOOCs using these cognitive models. In this work, we propose a Comprehension Factor Analysis model (CFM) for online courses, which integrates student reading interactions in student models to track and predict learning outcomes. Our model evaluation shows that CFM outperforms state-of-the-art models in predicting students' performance in a MOOC. These models can help better student-wise adaptation in the context of MOOCs.
Faster Placement of Virtual Machines through Adaptive Caching

Network Function Virtualization (NFV) allows operators to deploy network functions in virtual machines (VMs) and benefit from on-demand deployment. VMs are placed on one of the hosts in the cloud, and existing resource management algorithms assume full knowledge of the system's state. For large clusters, attaining the system's state creates bottlenecks and therefore it takes a long time to deploy network functionalities. Intuitively, placement can be accelerated if the resource management algorithm operates on a cached system state which is not entirely up to date, but the placement quality may suffer. Our work introduces a new cache refresh method that achieves an up to a 5.3x reduction in placement time with only a slight degradation of quality compared to having the complete and up to date system's state.
The In-Operation Drift Compensation of MEMS Gyroscope Based on Bagging-ELM and Improved CEEMDAN

Limited by the micro-electromechanical system (MEMS) fabrication technology, the in-operation drift of MEMS gyroscope which degrades measurement repeatability, accuracy, and stability has non-stationary wide-band components and a large difference between each power cycle. The drift limits the usability of MEMS gyroscope in a variety of field applications where autonomous and repeatable operation is required over a long time in harsh environmental conditions. Therefore, a novel method is proposed to compensate the drift. At first, an improved complete ensemble empirical mode decomposition is used to decompose the original signals into a series of intrinsic mode functions (IMFs), and the threshold de-noising method is adopted to filter the IMFs; then, the de-noised sub-series are reconstructed into training and testing dataset, respectively, and the bagging extreme learning machine-based model has been trained; finally, the compensation signal is predicted by the model with testing dataset, and the desired results can be obtained after compensation. The proposed method has been validated by a 9000-s in-operation experiment of CRG20 by comparing it with a typical method. The experiment demonstrated that the proposed method can enhance generalization performance and can boost compensation accuracy of the model, and the bias instability reduced from 0.0785°/s to 0.0046°/s.
Multiple Complementary Sequence Sets With Low Inter-Set Cross-Correlation Property

Complementary sequences have been applied to multi-carrier code-division multiple-access wireless communication systems. In this letter, multiple complementary sequence sets are constructed. The presented multiple complementary sequence sets have the following properties: first, each set is a complete complementary code (CCC) with parameters (p,p,p)-CCC; second, the maximum inter-set aperiodic cross-correlation amplitude is p; and finally, an aperiodic quasi-complementary sequence set with asymptotically near optimal correlation amplitude will be obtained by combining these complementary sequence sets together, which is not covered in the literature.
Talking trolls into existence: On the floor management of trolling in online forums

Much has been said about the problems of internet trolls in recent mainstream media reporting of online communication, yet surprisingly little empirical work has been conducted on the pragmatics of trolling. Existing studies of trolling come from different disciplines and provide different interpretations and definitions of trolls, leaving many questions about the phenomenon unanswered. The present study aims to narrow this empirical gap by providing a discourse-analytic account of online trolling. Specifically, the study utilizes the notion of floor spaces to uncover how online users discursively orient themselves to trolling behavior and why such responses vary from one message thread to another. The findings show that the extent to which an alleged troll is accepted into a discussion is partly related to how contributors discursively orient themselves to floor spaces, or in other words, co-establish appropriate forms and ways of communication. The finding that any given message can potentially be treated as an attempt to troll is helpful in understanding the extent to which trolling should be viewed as deviant behavior.
Routing in Black Box: Modularized Load Balancing for Multipath Data Center Networks

Multipath networks are widely used in data centers and load balancing is one of the most important technologies to improve their performances. Due to the ever-increasing in data center network size, existing load balancing algorithms face the challenges of efficiency and scalability. In this paper, we propose a new load balancing algorithm for large-scale, multi-tier fat-tree based data center networks. Different from the conventional architectures, a multi-tier fat-tree is divided into multiple routing domains according to the topology, and the routing processes in different domains are independent. The devices outside a routing domain can only access the specific interfaces provided by this domain. It is thus very convenient for deployment and modular upgrade. We also design a distributed and data-driven feedback mechanism, with which the routing decision is based on the global load information. We prove that the new algorithm can achieve perfect load balancing in multipath networks and show that the new algorithm outperforms all other load balancing algorithms in performance.