Reviewerly/exHarmony huggingface dataset:
PriRanGe: Privacy-Preserving Range-Constrained Intersection Query Over Genomic Data

Genomic data is being produced rapidly by both individuals and enterprises, and outsourcing this ever-increasing data into clouds is promising for cutting the cost of data owners and mining the wealth of genomic data at a larger scale. However, genome carries sensitive information about individuals, and it is challenging to securely and efficiently perform analysis on remotely hosted genomic databases. In this paper, we present a privacy-preserving range-constrained intersection query scheme on genomic data. To achieve security and efficiency, we propose a protocol to fulfill range-constrained intersection query, named PriRanGe. With PriRanGe, a client can securely query genomic data in a specific range in a database while keeping this whole process private. The security of our design targets genomic database confidentiality, query range/result confidentiality, and access pattern protection, and the advantage in efficiency is due to most employed primitives are symmetric. We thoroughly evaluated our design by security proof, experimental analysis and comparison to the state-of-the-art works, all of which support the conclusion that this design is both secure and fast.
Two-Stream Spatial Graphormer Networks for Skeleton-Based Action Recognition

In skeleton-based human action recognition, Transformer, which models the correlations between joint pairs in global topology, has achieved remarkable results. However, compared to many researches on changing graph topology learning in GCN, Transformer self-attention ignores the topology of the skeleton graph when capturing the dependencies between joints. To address these problems, we propose a novel two-stream spatial Graphormer network (2s-SGR), which models joint and bone information using self-attention incorporating structural encodings. First, in the joint stream, while Transformer models joint correlations in the global topology of the space, the topology of the joints and the edge information of the bones are introduced into the self-attention through custom structural encodings. At the same time, joint motion information is modeled in spatial-temporal blocks. The added information on structure and motion can effectively capture the dependencies of nodes between frames and enhance feature representation. Second, for the second-order information of the skeleton, the bone stream adapts to the structure of the bone by adjusting the custom structural encodings. Finally, the global spatial-temporal features of joints and bones in the skeleton are fused and input into the classification network to obtain action recognition results. Extensive experiments on three large-scale datasets, NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics, demonstrate that the performance of the 2s-SGR proposed in this paper is at the state-of-the-art level and is effectively validated by ablation experiments.
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics

Detection-driven real-time video analytics require continuous detection of objects contained in the video frames using deep learning models like YOLOV3, EfficientDet, etc. However, running these detectors on each and every frame in resource-constrained edge devices is computationally intensive. By taking the temporal correlation between consecutive video frames into account, we note that detection outputs tend to be overlapping in successive frames. Elimination of “similar” consecutive frames (the same set of objects with slightly offset bounding boxes) will lead to a negligible drop in performance while offering significant performance benefits by reducing overall computation and communication costs. The key technical questions are, therefore, (a) how to identify which frames to be processed by the object detector, and (b) how many successive frames can be skipped (called skip-length) once a frame is selected to be processed. The overall goal of the process is to keep the error due to skipping frames as small as possible. We introduce a novel error vs processing rate optimization problem with respect to the object detection task that balances between the error rate and the fraction of frames actually passed and processed. Subsequently, we propose an off-line Reinforcement Learning (RL)-based algorithm to determine these skip-lengths as a state-action policy of the RL agent from a recorded video and then deploy the agent online for live video streams. To this end, we develop FrameHopper, an edge-cloud collaborative video analytics framework, that runs a lightweight trained RL agent on the camera and passes filtered frames to the cloud/edge server where the object detection model runs for a set of applications. We have tested our approach on a number of live videos captured from real-life scenarios and show that FrameHopper processes only a handful of frames but produces detection results closer to the "oracle" solution and outperforms recent state-of-the-art solutions in most cases.
Online Waypoint Path Refinement for Mobile Robots Using Spatial Definition and Classification Based on Collision Probability

A novel online path refinement method is suggested for mobile robots, which refines the given path, including several waypoints using sensor data with a low computational load. The method periodically plans a waypoint path from the current robot position to a certain waypoint on the given waypoint path. While the robot moves along the given path, the path is continuously refined to avoid collisions and local minima caused by unmapped obstacles that are not represented on the map but sensed by sensors. Also, if a shortcut is detected while the robot is moving, the shortcut replaces the existing waypoint path. This online waypoint path refinement (OWPR) method is very practical in terms of computation time and memory size since it is performed regardless of the size of the workspace. To validate the actual performances of OWPR method, various simulations and experiments are conducted. As a result of the simulation and experiment, the maximum execution time of the OWPR algorithm is less than 10 ms, and the generated waypoint path shortened the overall travel distance without collision and falling to the local minima.
Publishing Asynchronous Event Times with Pufferfish Privacy

Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.
Digital Community Moderation Values: Politics, News, and Hot Beverages on Reddit

Across social media, major concerns over content moderation, policy ethics, and community values have revealed a pressing need for guidelines and boundaries for digital communication. By examining several of reddit’s forums, we show how these values translate into rules that aid moderators in scaffolding and maintaining their communities. Considering such codes of ethics as part of our work in internet studies, we use these codes and guidelines to uncover how these models can support healthy communities and resist bad actors.
UavSim: An Open-Source Simulator for Multiple UAV Path Planning

Though the primary method for evaluating multiple UAV path planning algorithms is simulation, there is no lightweight open-source software built to compare algorithms. As a result, most researchers develop their own simulation environments. The presence of many simulation environments makes evaluation of separately developed algorithms difficult. To introduce standardization into the multiple UAV path planning space, we have created an easy-to-use simulator for both development and evaluation of path planning algorithms. Our simulator focuses on the problem of small object detection using multiple UAVs. Its careful object-oriented design allows users unlimited flexibility in developing planning algorithms. UavSim is freely available on GitHub (https://github.com/rmaksymiuk/UavSim).
User-centered Design (UCD) and Transcreation of Non-profit Communications in a Technical Communication Classroom

Technical communication (TC) instruction is challenging given several dynamics including its interdisciplinary nature and that designing for usability requires a deep understanding of the target user and social and contextual factors that affect usability. Moreover, given that professional TC often involves international collaboration, teaching effective TC knowledge and skills becomes increasingly important. This presentation will describe the informally named, “Throw Them into the Fire” (TTITF) approach implemented in a TC classroom over the course of the Fall 2021 semester. The TTITF way of teaching TC involved work with a real-life client, UCD processes, and collaboration with international translation students to complete a TC project. The knowledge, assignments, activities, and interactions students had with each other, the client, and the translators will be discussed in detail providing academics and scholars an effective approach to teaching the messy, complicated, yet valuable TC principles and practices.
Team Roles &amp; Rhetorical Intelligence in Human-Machine Writing

This paper examines AI-based writing systems and how humans might partner with these systems to produce effective professional communication. We offer a taxonomy for examining roles in human-machine teaming for writing: Resource Tool, Assistant, Writer, and Executive Decision-Maker (whether at the beginning or end of the project). In particular, we focus on humanmachine teaming in relation to what we call rhetorical intelligence, the ability to invent and write for audience, purpose, and context. We examine human-machine writing by focusing on two cases: GameChanger and Phrazor by vPhrase. We conclude by proposing some guidelines for human-machine teaming for the production of professional communication.
A focus-based analysis of Nominative objects in Japanese

This paper explores the derivation of the sentence with Nominative objects in Japanese. The empirical data from standard and Kumamoto Japanese (a dialect spoken in the Kyushu region) reveal that Nominative objects can remain within the verb phrase. In the light of the scope interpretation observed in the previous studies, there should also be a case in which Nominative objects move out of the verb phrase. This paper proposes that Nominative objects may undergo movement to the TP domain to satisfy focus, which makes them take wide scope with respect to the potential suffix and negation. The scope interpretation of Nominative objects is thus attributed to their focus-related movement, in contrast to the previous approaches. This paper also investigates how Case assignment takes place in Japanese, and proposes that an unvalued Case-feature in Japanese enters upwardly into an agreement relation with heads that have a valued Case-feature, as a result of which DPs obtain Case in Japanese. This proposal allows objects within the verb phrase to receive Nominative Case without moving to the TP domain, insofar as a v*P phase is canceled. Finally, the proposed analysis is extended to the derivation of Dative subject constructions in Japanese.
A simulator of optical coherent-state evolution in quantum key distribution systems

Abstract Quantum key distribution (QKD) is believed to represent a viable solution to achieve theoretically unconditionally secure key generation. However, the available optical systems for experimental QKD, based on photon transmission, are flawed by non-idealities that ultimately limit the achievable performance. Classical simulation of the optical hardware employed in these systems may take on a determining role in engineering future QKD networks. In this article, attempts for developing a QKD simulator based on low-computational-cost models of the employed hardware are presented. In particular, the simulation infrastructure targets polarization-based QKD setups with faint laser sources, whose behaviour can be described by semiclassical coherent states and Mean Photon Number (MPN) per beam. The effects of passive optical components on the photonic qubit evolution are described by Jones matrices, whose coefficients, for some commercial devices, are stored in an ad-hoc library. Realistic eavesdropping attacks and non-idealities, such as optical losses, fibre attenuation, polarization misalignment and limited efficiency of single-photon detectors, are also taken into account. The infrastructure allows the user to describe the desired QKD configuration and it provides in output the MPN at the receiver and two fiducial performance parameters: Quantum Bit Error Rate (QBER) and secure key rate. The comparison of the simulation results with experimental data in the state-of-the-art literature highlights that this work is a step forward towards the definition of compact models for the hardware-dependent simulation of quantum-assisted communication networks.
Unsupervised SMT: an analysis of Indic languages and a low resource language

The rapid globalisation in language technology and the Internet’s fast expansion have brought nations and their cultures close together, and the demand for inter-language interactions has risen enormously. However, in many low-resource languages (LRL) pairings and areas, Machine Translation (MT) is still not viable because of a lack of parallel data. The challenge of MT is still unsolved. Recent studies employing monolingual datasets have shown excellent outcomes in Phrase-based Statistical MT (PBSMT) and Neural MT (NMT) systems. However, earlier researchers have demonstrated that unsupervised Statistical MT surpasses unsupervised NMT, especially for different language pairings. The study unveils the compendium of ten unsupervised SMT systems translation tasks utilizing a monolingual dataset from the Dravidian and Indo-Aryan language families; and a low-resource endangered language. The machine-translated experimental outcomes examined the system using different tokenizers and investigated them for various language pairs using different evaluation metrics for various iterations. The statistical significance of test results has been computed for each evaluation metric to check the true system quality of the translation tasks.
Blockchain Network: Performance Optimization

Blockchain Technology is one of the most disruptive technologies of present days. This technology eliminates the need of central controlling authority. It provides the decentralized authentication, integrity check of submitted records and ensures the trust over the network. Due to long execution time of Blockchain records, overall throughput of Blockchain network gets affected badly. In this paper, we analyzed the Caliper Hyperledger tool and analyzed the performance of the Blockchain network in various scenarios. We discussed various key parameters which affect the Blockchain performance and finally, we proposed various guidelines for optimizing the Blockchain performance.
Modified Bat Algorithm for Balancing Load of Optimal Virtual Machines in Cloud Computing Environment

Load balancing represents a key issue in any job scheduling application to guarantee optimal utilization of underlying resources, enhance performance, and reduce the execution cost. The core of this paper is the fitness value calculation (FVC) to provide application software, a reliable solution in the early stages of load balancing. This study is focused on the cloud computing environment. It includes both physical components (in particular cloud infrastructure and cloud storage) and logical components (in particular cloud services and cloud platforms). Such complex structure is interconnected to deliver services to users and achieve improved performance of the whole system. This case study is one of the most important segments of cloud computing, i.e., load balancing. The aim of this paper is to introduce a new approach to balance the load among virtual machines of the cloud computing environment. The proposed method led to the proposal and implementation of an algorithm inspired by Bat Algorithm. This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines. The proposed algorithm works in two variants: Modified Bat Algorithm with 'Overloaded Optimal Virtual Machine' (MBA-OOVM) and Modified Bat Algorithm with 'Balanced Virtual Machine' (MBA-BVM). MBA generates solutions which are cost-effective and the strengths of MBA are finally validated by comparing it with Bat Algorithm.
Standard Verification using Model Based Systems Engineering

Abstract Systems engineering has embraced MBSE and other digital engineering techniques that have been in use in software engineering for decades. One area that MBSE lacks is automation of verification and validation of requirements for standards. System models require rework to model standards and in the case of reuse, extensive verification to ensure they have been implemented properly. SysML can utilize architectural constraints for system models in a similar way to how software engineers use architectural constraints in software architectures. Architectural constraints can be used to verify conformance with architectural decisions and standard implementation. This tool can be applied to automated standard compliance verification using query functions for analysis or validation suites for fast verification of architectural constraints. This technique has been applied to larger Reference architectures with success in finding errors architecture design as well.
Collision and obstacle avoidance strategy for multi-agent systems with velocity dynamic programing

This paper conducts research on collision and obstacle avoidance of multi-agent systems without mapping ability, while the constrained agent can only detect obstacles within a limited distance, then a velocity programing strategy is proposed considering the lack of a high-resolution map and the challenge of the modeling of complex obstacles. Based on the detecting information of nearby members and obstacles, a discontinuous velocity programing space is constructed by imposing the constraints on the velocity. To obtain expansive programing space, two different ways are utilized to establish the velocity constraints of avoiding various obstacles. For obstacles that can be viewed as virtual circular obstacles, a barrier function is introduced to restrict the radial component of the velocity. As for the obstacle that can only be detected partially, we use the border lines to construct a velocity feasible domain, and the domain is approximated by the polygonal region using the convex theory. Then, the nominal velocity is utilized as the objective and a nonlinear dynamic programing regulator is proposed. Furtherly, velocity limits generated from the system kinematic constraints are incorporated into the regulator. Finally, three tests are carried out and the feasibility of the proposed regulator is verified.
Sensitivity and uniformity in statistical randomness tests

In this paper we propose a new measure of the sensitivity of a statistical test . This can be applied to any of the common statistical batteries (NIST SP 800-22, Dieharder, TestU01, ENT, all FIPS versions, etc.) We will validate its use with the FIPS 140-2 battery. In addition, we will apply this new measure to other statistical tests, including tests of independence and uniformity. The latter leads to a new test battery, not previously seen in the literature to the best of our knowledge, that can be very powerful as a sequence checking tool. This new proposal is later tested to judge its potential as a battery for cryptographic use.
Lifelong Adaptive Machine Learning for Sensor-Based Human Activity Recognition Using Prototypical Networks

Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people’s everyday behavior. Current research in CL applied to the HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on five publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.
Diversity Concepts in Computer Science and Technology Development: A Critique

Diversity has become increasingly relevant in computer science and technology development, both in terms of inclusive design teams and as a concept used to design “diversity-aware” technologies. The latter part has received less attention and notions of diversity leveraged to design technologies remain understudied. This paper critically examines diversity concepts employed by computer scientists and designers by reviewing 120 articles in two subfields of computer science: personalized recommendations and human–computer interaction. Drawing on Black feminism and critical race theory-inspired literature, I offer a critique of demographic and cultural user representations and underscore the shortcomings of diversity as a strategy to increase user satisfaction and fairness. Particularly concerning is the unreflected use of binary, static, and individual-level diversity concepts in the design of technologies. Such concepts render structural inequalities between and within groups of users invisible and thereby risk reinforcing existing injustices. The paper closes with considerations on whether and how diversity concepts can be leveraged by designers in a social justice–oriented manner.