OpenRaiser/Intern-Atlas huggingface dataset:
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation state-of-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.
Spanish philosophers perceptions of pay to publish and open access: books versus journals, more than a financial dilemma
Venue: arXiv cs.DL 2021
Year: 2021

This study examines habits and perceptions related to pay to publish and open access practices in fields that have attracted little research to date: philosophy and ethics. The study is undertaken in the Spanish context, where the culture of publication and the book and journal publishing industry has some specific characteristics with regard to paying to publish, such as not offering open access distribution of books published for a fee. The study draws on data from a survey of 201 researchers, a public debate with 26 researchers, and 14 in-depth interviews. The results reveal some interesting insights on the criteria researchers apply when selecting publishers and journals for their work, the extent of paying to publish (widespread in the case of books and modest for journals) and the debates that arise over the effects it has on manuscript review and unequal access to resources to cover publication fees. Data on the extent of open access and the researchers views on dissemination of publicly funded research are also presented.
A fast vectorized sorting implementation based on the ARM scalable vector extension (SVE)
Venue: arXiv cs.DC 2021
Year: 2021

The way developers implement their algorithms and how these implementations behave on modern CPUs are governed by the design and organization of these. The vectorization units (SIMD) are among the few CPUs' parts that can and must be explicitly controlled. In the HPC community, the x86 CPUs and their vectorization instruction sets were de-facto the standard for decades. Each new release of an instruction set was usually a doubling of the vector length coupled with new operations. Each generation was pushing for adapting and improving previous implementations. The release of the ARM scalable vector extension (SVE) changed things radically for several reasons. First, we expect ARM processors to equip many supercomputers in the next years. Second, SVE's interface is different in several aspects from the x86 extensions as it provides different instructions, uses a predicate to control most operations, and has a vector size that is only known at execution time. Therefore, using SVE opens new challenges on how to adapt algorithms including the ones that are already well-optimized on x86. In this paper, we port a hybrid sort based on the well-known Quicksort and Bitonic-sort algorithms. We use a Bitonic sort to process small partitions/arrays and a vectorized partitioning implementation to divide the partitions. We explain how we use the predicates and how we manage the non-static vector size. We explain how we efficiently implement the sorting kernels. Our approach only needs an array of O(log N) for the recursive calls in the partitioning phase, both in the sequential and in the parallel case. We test the performance of our approach on a modern ARMv8.2 and assess the different layers of our implementation by sorting/partitioning integers, double floating-point numbers, and key/value pairs of integers. Our approach is faster than the GNU C++ sort algorithm by a speedup factor of 4 on average.
Designer-User Communication for XAI: An epistemological approach to discuss XAI design
Venue: arXiv cs.AI 2021
Year: 2021

Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task. Most of the available frameworks and methods for XAI focus on data scientists and ML developers as users. Our research is about XAI for end-users of AI systems. We argue that we need to discuss XAI early in the AI-system design process and with all stakeholders. In this work, we aimed at investigating how to operationalize the discussion about XAI scenarios and opportunities among designers and developers of AI and its end-users. We took the Signifying Message as our conceptual tool to structure and discuss XAI scenarios. We experiment with its use for the discussion of a healthcare AI-System.
TopicsRanksDC: Distance-based Topic Ranking applied on Two-Class Data
Venue: arXiv cs.IR 2021
Year: 2021

In this paper, we introduce a novel approach named TopicsRanksDC for topics ranking based on the distance between two clusters that are generated by each topic. We assume that our data consists of text documents that are associated with two-classes. Our approach ranks each topic contained in these text documents by its significance for separating the two-classes. Firstly, the algorithm detects topics using Latent Dirichlet Allocation (LDA). The words defining each topic are represented as two clusters, where each one is associated with one of the classes. We compute four distance metrics, Single Linkage, Complete Linkage, Average Linkage and distance between the centroid. We compare the results of LDA topics and random topics. The results show that the rank for LDA topics is much higher than random topics. The results of TopicsRanksDC tool are promising for future work to enable search engines to suggest related topics.
Post-war Civil War Propaganda Techniques and Media Spins in Nigeria and Journalism Practice
Venue: arXiv cs.CY 2021
Year: 2021

In public relations and political communication, a spin is a form of propaganda achieved through knowingly presenting a biased interpretation of an event or issues. It is also the act of presenting narratives to influence public opinion about events, people or and ideas. In war time, various forms of spins are employed by antagonists to push their brigades to victory and wear out the opponents. During the Nigerian civil war, quite a number of these spins were dominant for example GOWON (Go On With One Nigeria); On Aburi We Stand, O Le Ku Ija Ore. Post-war years presented different spins and fifty years after the war, different spins continue to push emerging narratives (e.g. marginalization, restructuring. This paper investigates and analyzes the different propaganda techniques and spins in the narratives of the Nigerian civil in the past five years through a content analysis of three national newspapers: The Nigerian Tribune, Daily Trust and Sun Newspapers. Findings confirm that propaganda and spins are not limited to war time, but are actively deployed in peace time. This development places additional challenge on journalists to uphold the canons of balance, truth and fairness in reporting sensitive national issues.
"Hey Alexa, What do You Know About the COVID-19 Vaccine?" -- (Mis)perceptions of Mass Immunization Among Voice Assistant Users
Venue: arXiv cs.CY 2021
Year: 2021

In this paper, we analyzed the perceived accuracy of COVID-19 vaccine information spoken back by Amazon Alexa. Unlike social media, Amazon Alexa doesn't apply soft moderation to unverified content, allowing for use of third-party malicious skills to arbitrarily phrase COVID-19 vaccine information. The results from a 210-participant study suggest that a third-party malicious skill could successful reduce the perceived accuracy among the users of information as to who gets the vaccine first, vaccine testing, and the side effects of the vaccine. We also found that the vaccine-hesitant participants are drawn to pessimistically rephrased Alexa responses focused on the downsides of the mass immunization. We discuss solutions for soft moderation against misperception-inducing or altogether COVID-19 misinformation malicious third-party skills.
An Extensive Analytical Approach on Human Resources using Random Forest Algorithm
Venue: arXiv cs.CY 2021
Year: 2021

The current job survey shows that most software employees are planning to change their job role due to high pay for recent jobs such as data scientists, business analysts and artificial intelligence fields. The survey also indicated that work life imbalances, low pay, uneven shifts and many other factors also make employees think about changing their work life. In this paper, for an efficient organisation of the company in terms of human resources, the proposed system designed a model with the help of a random forest algorithm by considering different employee parameters. This helps the HR department retain the employee by identifying gaps and helping the organisation to run smoothly with a good employee retention ratio. This combination of HR and data science can help the productivity, collaboration and well-being of employees of the organisation. It also helps to develop strategies that have an impact on the performance of employees in terms of external and social factors.
Correlations Between Learning Environments and Dropout Intention
Venue: arXiv cs.CY 2021
Year: 2021

This research is comparing learning environments to students dropout intentions. While using statistics I looked at data and the correlations between two articles to see how the two studies looked side to side. Learning environments and dropout intentions can both have vary effects on students. They can both determine if a student does well, or bad in school especially math.
An Enterprise Architecture Framework for E-learning
Venue: arXiv cs.CY 2021
Year: 2021

With a trend toward becoming more and more information and communication based, learning services and processes were also evolved. E-learning comprises all forms of electronically supported learning and teaching. The information and communication systems serve as a fundamental role to implement these learning processes. In the typical information-driven organizations, the E-learning is part of a much larger platform for applications and data that extends across the Internet and intranet/extranet. In this respect, E-learning has brought about an inevitable tendency to lunge towards organizing their information based activities in a comprehensive way. Building an Enterprise Architecture (EA) undoubtedly serves as a fundamental concept to accomplish this goal. In this paper, we propose an EA for E-learning information systems. The presented framework helps developers to design and justify completely integrated learning and teaching processes and information systems which results in improved pedagogical success rate.
Application of Deep Self-Attention in Knowledge Tracing
Venue: arXiv cs.LG 2021
Year: 2021

The development of intelligent tutoring system has greatly influenced the way students learn and practice, which increases their learning efficiency. The intelligent tutoring system must model learners' mastery of the knowledge before providing feedback and advices to learners, so one class of algorithm called "knowledge tracing" is surely important. This paper proposed Deep Self-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online assessment system used by students in many universities in China, to help these students learn more efficiently. Experimentation on the data of PTA shows that DSAKT outperforms the other models for knowledge tracing an improvement of AUC by 2.1% on average, and this model also has a good performance on the ASSIST dataset.
Mining Architecture Tactics and Quality Attributes Knowledge in Stack Overflow
Venue: arXiv cs.SE 2021
Year: 2021

Context: Architecture Tactics (ATs) are architectural building blocks that provide general architectural solutions for addressing Quality Attributes (QAs) issues. Mining and analyzing QA-AT knowledge can help the software architecture community better understand architecture design. However, manually capturing and mining this knowledge is labor-intensive and difficult. Objective: Using Stack Overflow (SO) as our source, our main goals are to effectively mine such knowledge; and to have some sense of how developers use ATs with respect to QA concerns from related discussions. Methods: We applied a semi-automatic dictionary-based mining approach to extract the QA-AT posts in SO. With the mined QA-AT posts, we identified the relationships between ATs and QAs. Results: Our approach allow us to mine QA-AT knowledge effectively with an F-measure of 0.865 and Performance of 82.2%. Using this mining approach, we are able to discover architectural synonyms of QAs and ATs used by designers, from which we discover how developers apply ATs to address quality requirements. Conclusions: We make two contributions in this work: First, we demonstrated a semi-automatic approach to mine ATs and QAs from SO posts; Second, we identified little-known design relationships between QAs and ATs and grouped architectural design considerations to aid architects make architecture tactics design decisions.
AI Enabled Data Quality Monitoring with Hydra
Venue: arXiv cs.CY 2021
Year: 2021

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how "off-the-shelf" technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.
Pedestrian Path Modification Mobile Tool for COVID-19 Social Distancing for Use in Multi-Modal Trip Navigation
Venue: arXiv cs.CY 2021
Year: 2021

The novel Corona virus pandemic is one of the biggest worldwide problems right now. While hygiene and wearing masks make up a large portion of the currently suggested precautions by the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO), social distancing is another and arguably the most important precaution that would protect people since the airborne virus is easily transmitted through the air. Social distancing while walking outside, can be more effective, if pedestrians know locations of each other and even better if they know locations of people who are possible carriers. With this information, they can change their routes depending on the people walking nearby or they can stay away from areas that contain or have recently contained crowds. This paper presents a mobile device application that would be a very beneficial tool for social distancing during Coronavirus Disease 2019 (COVID-19). The application works, synced close to real-time, in a networking fashion with all users obtaining their locations and drawing a virtual safety bubble around them. These safety bubbles are used with the constant velocity pedestrian model to predict possible future social distancing violations and warn the user with sound and vibration. Moreover, it takes into account the virus staying airborne for a certain time, hence, creating time-decaying non-safe areas in the past trajectories of the users. The mobile app generates collision free paths for navigating around the undesired locations for the pedestrian mode of transportation when used as part of a multi-modal trip planning app. Results are applicable to other modes of transportation also. Features and the methods used for implementation are discussed in the paper. The application is tested using previously collected real pedestrian walking data in a realistic environment.
A New Vertex Connectivity Metric
Venue: arXiv cs.DS 2021
Year: 2021

A new metric for quantifying pairwise vertex connectivity in graphs is defined and an implementation presented. While general in nature, it features a combination of input features well-suited for social networks, including applicability to directed or undirected graphs, weighted edges, and computes using the impact from all-paths between the vertices. Moreover, the $O(V+E)$ method is applicable to large graphs. Comparisons with other techniques are included.
SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
Venue: arXiv cs.CL 2021
Year: 2021

Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach. The proposed multi-pass model shares a single encoder between the domain information and dialogue utterance. The domain's description represents the query and the dialogue utterance serves as the context. The model improves performance on unseen services by at least 1.6x compared to single-pass baseline models on the SGD dataset. SGD-QA shows competitive performance compared to state-of-the-art multi-pass models while being significantly more efficient in terms of memory consumption and training performance. We provide a thorough discussion on the model with ablation study and error analysis.
The Boombox: Visual Reconstruction from Acoustic Vibrations
Venue: arXiv cs.CV 2021
Year: 2021

Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical. While robots often use cameras for state estimation, the visual modality is not always ideal due to occlusions and poor illumination. We introduce The Boombox, a container that uses sound to estimate the state of the contents inside a box. Based on the observation that the collision between objects and its containers will cause an acoustic vibration, we present a convolutional network for learning to reconstruct visual scenes. Although we use low-cost and low-power contact microphones to detect the vibrations, our results show that learning from multimodal data enables state estimation from affordable audio sensors. Due to the many ways that robots use containers, we believe the box will have a number of applications in robotics. Our project website is at: boombox.cs.columbia.edu
A Measure of Research Taste
Venue: arXiv cs.DL 2021
Year: 2021

Researchers are often evaluated by citation-based metrics. Such metrics can inform hiring, promotion, and funding decisions. Concerns have been expressed that popular citation-based metrics incentivize researchers to maximize the production of publications. Such incentives may not be optimal for scientific progress. Here we present a citation-based measure that rewards both productivity and taste: the researcher's ability to focus on impactful contributions. The presented measure, CAP, balances the impact of publications and their quantity, thus incentivizing researchers to consider whether a publication is a useful addition to the literature. CAP is simple, interpretable, and parameter-free. We analyze the characteristics of CAP for highly-cited researchers in biology, computer science, economics, and physics, using a corpus of millions of publications and hundreds of millions of citations with yearly temporal granularity. CAP produces qualitatively plausible outcomes and has a number of advantages over prior metrics. Results can be explored at https://cap-measure.org/
Multiclass Classification using dilute bandit feedback
Venue: arXiv cs.LG 2021
Year: 2021

This paper introduces a new online learning framework for multiclass classification called learning with diluted bandit feedback. At every time step, the algorithm predicts a candidate label set instead of a single label for the observed example. It then receives feedback from the environment whether the actual label lies in this candidate label set or not. This feedback is called "diluted bandit feedback". Learning in this setting is even more challenging than the bandit feedback setting, as there is more uncertainty in the supervision. We propose an algorithm for multiclass classification using dilute bandit feedback (MC-DBF), which uses the exploration-exploitation strategy to predict the candidate set in each trial. We show that the proposed algorithm achieves O(T^{1-\frac{1}{m+2}}) mistake bound if candidate label set size (in each step) is m. We demonstrate the effectiveness of the proposed approach with extensive simulations.
VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living
Venue: arXiv cs.CV 2021
Year: 2021

Many attempts have been made towards combining RGB and 3D poses for the recognition of Activities of Daily Living (ADL). ADL may look very similar and often necessitate to model fine-grained details to distinguish them. Because the recent 3D ConvNets are too rigid to capture the subtle visual patterns across an action, this research direction is dominated by methods combining RGB and 3D Poses. But the cost of computing 3D poses from RGB stream is high in the absence of appropriate sensors. This limits the usage of aforementioned approaches in real-world applications requiring low latency. Then, how to best take advantage of 3D Poses for recognizing ADL? To this end, we propose an extension of a pose driven attention mechanism: Video-Pose Network (VPN), exploring two distinct directions. One is to transfer the Pose knowledge into RGB through a feature-level distillation and the other towards mimicking pose driven attention through an attention-level distillation. Finally, these two approaches are integrated into a single model, we call VPN++. We show that VPN++ is not only effective but also provides a high speed up and high resilience to noisy Poses. VPN++, with or without 3D Poses, outperforms the representative baselines on 4 public datasets. Code is available at https://github.com/srijandas07/vpnplusplus.