Reviewerly/exHarmony huggingface dataset:
EVALUATING THE SENIOR EMPLOYMENT AND SOCIAL ACTIVITY SUPPORT PROGRAM IN KOREA: A SYSTEMATIC REVIEW OF SATISFACTION

Abstract For older people, jobs provide not only income security, but also a social role. Jobs also give them a sense of subjective security in terms of maintaining self-esteem and self-identity, and improve their overall life satisfaction. The Senior Employment and Social Activity Support Program (SESAP) provides for older Koreans for supplemental income security and social participation in old age by creating and providing social jobs that match the aptitudes and abilities of the senior citizens. It has been 20 years since the SESAP was introduced, and many studies have accumulated to evaluate the program in many different aspects. This study aims to summarize the existing studies focusing on satisfaction with program among participants. A systematic literature review was conducted to identify empirical research studies written in Korean and published in a peer-reviewed journal between 2004 and 2023. To this end, we searched through RISS, KISS, and DBpia databases using the selected keywords and obtained 126 articles. After removing duplicates and excluding gray literature, 26 articles were selected and analyzed through a peer-review process. Analysis of the selected literature shows that a majority of the program participants are women, and satisfaction with salary was lower compared to other types of program satisfaction (i.e., job content, work environment, and relationship with co-workers). Lastly, satisfaction with program are associated with other indicators such as better life satisfaction, and quality of life. This systematic review provides suggestions for further research to improve the satisfaction with SESAP among participants.
Procedural metadata for geographic information using an algebra of core concept transformations

Transformations are essential for dealing with geographic information. They are involved not only in the conversion between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relation types, and a set of basic transformations described in terms of function signatures that use such types. We enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions and inferring goal concepts across a workflow. In this article, we show how such procedural metadata can be used to retrieve workflows based on task descriptions derived from geo-analytical questions. Transformations can be queried independently from their implementations or data formats.
Intent and Entity Detection with Data Augmentation for a Mental Health Virtual Assistant Chatbot

We report on implementing MIRA, a mental health resource chatbot to support healthcare workers in finding timely and relevant mental health resources. To generate appropriate queries to our carefully curated resource database, the chatbot must correctly identify the intents of an interlocutor and extract relevant entities from the conversation. With insufficient labelled examples, we employ data augmentation to generate training data automatically. Moreover, instead of detecting intent and extracting entities independently with two different classifiers, we integrate the two tasks by taking advantage of their interdependencies obtaining 99% accuracy for intent detection and 95.4% accuracy in entity extraction.
Design of English Translation Intelligent Recognition Model Based on Markov Model

The method of English machine translation based on syntactic analysis cannot solve the problem of low accuracy of machine translation caused by partial structural ambiguity of massive English languages in intelligent identification technology. Therefore, based on the analysis of the application of intelligent machine translation tools in English translation, an English machine translation model based on modern intelligent recognition technology is designed and implemented. Polysemy is common in natural language, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key problem in the field of natural language processing. The goal of word sense disambiguation is to enable the machine to automatically judge the exact meaning of ambiguous words in a specific context. It plays an important role in information retrieval, topic tracking, text classification and machine translation. Aiming at the problems of complex working process and low scoring accuracy of oral English examination scoring system, an oral English examination scoring system based on Hidden Markov model is constructed. The speech recognition module and scoring process are designed. The speech recognizer and hidden Markov scoring model are used to realize the recognition, error judgment and scoring of oral English pronunciation, And the final score of oral English is obtained by changing the hundred mark system. Through direct maximum entropy model training, the relevant parameters are obtained to obtain the best combination of different English language features, solve the problem of partial structural ambiguity in a large number of English languages, and improve the accuracy of English machine translation.
MNHOKA - PPA Efficient M-Term Non-Homogeneous Hybrid Overlap-free Karatsuba Multiplier for GF (2<sup>n</sup>) Polynomial Multiplier

In the constantly evolving field of multiplication architectures, the Karatsuba algorithm and its extensions have captivated the minds of researchers with their performance metrics. One such optimized design is the Overlap-free Karatsuba (OKA) algorithm which has emerged as an innovative architecture, specifically aimed at enhancing power, performance, and area (PPA) parameters. In this paper, we introduce a novel technique referred to as M-term Non-Homogeneous Hybrid Overlap-free Karatsuba polynomial multiplier (MNHOKA), which surpasses existing state-of-the-art (SOTA) designs, including Karatsuba multiplier (KA), M-Term Karatsuba-like multiplier (MKA), Composite M-term Karatsuba-like multiplier (CMKA), and Overlap-free Karatsuba multiplier (OKA), across various operand sizes. In this paper, a detailed analysis of the proposed MNHOKA and its corresponding M-Term Non-homogeneous Hybrid Karatsuba Algorithm (MNHKA) is presented, highlighting its performance improvements on both Cadence 45 nm process and the ZYNQ ZCU-104 FPGA board for popular bit widths. In ASIC implementations, MNHOKA achieves significant ADP improvements of 28.33%, 28.99%, 58.23%, and 11.95% for operand sizes of 128, 232, 282, and 750 bits, respectively, compared to the best-case SOTA design. Furthermore, our method yields lower power consumption. When comparing FPGA results of the proposed MNHKA design with the best-case SOTA works, ADP improvement of 22.72%, 16.10%, 2.52%, and 11.36% improvement was achieved for the respective bit-widths of 128, 232, 282, and 750 bits respectively. The advantages of the proposed MNHOKA, along with its equivalent MNHKA design variants, are evident in their superior hardware characteristics over existing SOTA designs. This research represents a significant step towards realizing efficient Cryptosystems in the immediate future. To foster further research and innovation, we have made the hardware design files freely available to the researchers and designer community.
Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting

We investigate one of the most basic problems in streaming algorithms: approximating the number of elements in the stream. Famously, [Mor78] gave a randomized algorithm achieving a constant-factor approximation error for streams of length at most N in space $O(\log\log N)$. We investigate the pseudo-deterministic complexity of the problem and prove a tight $\Omega(\log N)$ lower bound, thus resolving a problem of [GGMW20].
Faster Algorithms for Text-to-Pattern Hamming Distances

We study the classic Text-to-Pattern Hamming Distances problem: given a pattern P of length m and a text T of length n, both over a polynomial-size alphabet, compute the Hamming distance between P and $T[i \ldots i+m-1]$ for every shift i, under the standard Word-RAM model with $\Theta(\log n)$-bit words.•We provide an $O(n \sqrt{m})$ time Las Vegas randomized algorithm for this problem, beating the decades-old $O(n \sqrt{m \log m})$ running time [Abrahamson, SICOMP 1987]. We also obtain a deterministic algorithm, with a slightly higher $O\left(n \sqrt{m}(\log m \log \log m)^{1 / 4}\right)$ running time. Our randomized algorithm extends to the k-bounded setting, with running time $O\left(n+\frac{n k}{\sqrt{m}}\right)$, removing all the extra logarithmic factors from earlier algorithms [Gawrychowski and Uznanski, ICALP 2018; Chan, Golan, Kociumaka, Kopelowitz and Porat, STOC 2020].•For the $(1+\varepsilon)$-approximate version of Text-to-Pattern Hamming Distances, we give an $\widetilde{O}\left(\varepsilon^{-0.93} n\right)$ time Monte Carlo randomized algorithm (where $\widetilde{O}$ hides poly-logarithmic factors), beating the previous $\widetilde{O}\left(\varepsilon^{-1} n\right)$ running time [Kopelowitz and Porat, FOCS 2015; Kopelowitz and Porat, SOSA 2018].Our approximation algorithm exploits a connection with 3SUM, and uses a combination of Fredman’s trick, equality matrix product, and random sampling; in particular, we obtain new results on approximate counting versions of 3 SUM and Exact Triangle, which may be of independent interest. Our exact algorithms use a novel combination of hashing, bit-packed FFT, and recursion; in particular, we obtain a faster algorithm for computing the sumset of two integer sets, in the regime when the universe size is close to quadratic in the number of elements. We also prove a fine-grained equivalence between the exact Text-to-Pattern Hamming Distances problem and a range-restricted, counting version of 3 SUM.
Tight Time-Space Lower Bounds for Constant-Pass Learning

In his breakthrough paper, Raz showed that any parity learning algorithm requires either quadratic memory or an exponential number of samples [FOCS’16, JACM’19]. A line of work that followed extended this result to a large class of learning problems. Until recently, all these results considered learning in the streaming model, where each sample is drawn independently, and the learner is allowed a single pass over the stream of samples. Garg, Raz, and Tal [CCC’19] considered a stronger model, allowing multiple passes over the stream. In the 2-pass model, they showed that learning parities of size n requires either a memory of size $n^{1.5}$ or at least $2^{\sqrt{n}}$ samples. (Their result also generalizes to other learning problems.) In this work, for any constant q, we prove tight memory-sample lower bounds for any parity learning algorithm that makes q passes over the stream of samples. We show that such a learner requires either $\Omega\left(n^{2}\right)$ memory size or at least $2^{\Omega(n)}$ samples. Beyond establishing a tight lower bound, this is the first nontrivial lower bound for q-pass learning for any $q \geq 3$. Similar to prior work, our results extend to any learning problem with many nearly-orthogonal concepts.We complement the lower bound with an upper bound, showing that parity learning with q passes can be done efficiently with $O\left(n^{2} / \log q\right)$ memory.
ScoutSL: An Open-Source Simulink Search Engine

Simulink is one of the most widely used modelling languages in safety-critical industries. Most models created in industrial settings are valuable intellectual property to their companies and are thus often not publicly available. But to study Simulink software engineering processes or to develop new Simulink tools, access to models with relevant properties is vital for researchers. We conducted a community survey to find out what kind of models and model metrics are of interest to researchers. With these results, we implemented ScoutSL (http://scoutsl.net), a tool that gives researchers easy online access to over 100k open-source Simulink models from which they can select a subset according to their needs. A short video demonstration is available online at https://youtu.be/HwsHL8LrVCM
Simply-Track-and-Refresh: Efficient and Scalable Rowhammer Mitigation

Rowhammer is a memory vulnerability that can compromise system-level security. Rowhammer occurs when a DRAM row is accessed repeatedly, potentially causing bit-flips for neighboring rows. The threshold for Rowhammer has decreased from 139K accesses in 2014 to 3.2K in 2022. This threshold is projected to decrease further. Many existing solutions are not scalable, incur high overhead, or fail to offer protection in realistic scenarios. We propose Simply-Track-And-Refresh (STAR) as an effective and scalable Rowhammer mitigation. We compare STAR's performance overhead to recent solutions, HYDRA and AQUA. At ultra-low thresholds (500), STAR introduces 9.5x/31.7x lower average execution time overhead than HYDRA/AQUA. In addition, STAR introduces up to 4.3x lower area overhead and up to 3.3x lower power consumption compared to HYDRA and AQUA. We present proof of correctness, area and power consumption results derived using CACTI, and evaluation results from the PARSEC, SPLASH-2, SPEC2006, SPEC2017, and PAMPAR benchmark suites.
MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach.
Discrete States-Based Trajectory Planning for Nonholonomic Robots

Due to nonholonomic dynamics, the motion planning of nonholonomic robots is always a difficult problem. This letter presents a Discrete States-based Trajectory Planning(DSTP) algorithm for autonomous nonholonomic robots. The proposed algorithm represents the trajectory as x and y positions, orientation angle, longitude velocity and acceleration, curvature, and time intervals. More variables make the expression of optimization and constraints simpler, reduce the error caused by too many approximations, and also handle the gear shifting situation. L-BFGS-B is used to deal with the optimization of many variables and box constraints, thus speeding up the problem solving. Various simulation experiments compared with prior works have validated that our algorithm has an order-of-magnitude efficiency advantage and can generate a smoother trajectory with a high speed and low control effort. Besides, real-world experiments are also conducted to verify the feasibility of our algorithm in real scenes. We will release our codes as ros packages.
A Collision Avoidance Path Planning Method for 7-DOF Space Manipulator<sup>*</sup>

This paper proposes a collision avoidance path planning approach for redundant robotic arms based on the RRT algorithm. The study focuses on a 7-DOF robotic arm with an SSRMS configuration. The method involves improved RRT-based path planning in configuration space (C-space) and collision detection in Cartesian space. The results of collision detection are employed to guide the path planning process. Emulating the SSRMS configuration robotic arm, a kinematic model is established. By enhancing the RRT algorithm with the following points: probabilistic sampling towards the target configuration, guided exploration near existing tree nodes, parent node reassignment for new nodes, and the adoption of a bidirectional searching tree, the randomness of sampling is reduced and the planning process is accelerated. By continuously mapping points on the surface of the arm to both the base coordinate system and the self-collision coordinate system, collision detection and self-collision detection are performed in the Cartesian space using a combination of surface equations and projection methods. Finally, a series of simulation experiments were conducted to analyze the impact of various parameters, different obstacles and different methods on the planning process. This validation confirmed the feasibility and effectiveness of the approach.
Design of a Surgical Human-Machine Natural Interaction Assistance System based on Holographic Reality

Ideally, intelligent surgery should allow surgeons to achieve insight, prediction, and assessment of the full range and course of diseased tissue without causing perceptual and interactive inconvenience. However, in the clinical setting, surgeons can only achieve judgment of the condition by continuously exposing patient tissues and organs through dissection, which is risky. To address these issues, this paper proposes a surgical human-machine natural interaction assistance system based on Holographic Reality. First, we annotate and segment the patient’s medical images and plan the surgical path to generate a knowledge-based 3D digital human model containing information about the tissue structure of the lesion and the access to the surgical instruments. Secondly, we introduce techniques such as motion recognition and force feedback into the virtual reality environment for holographic display and somatosensory interaction, achieving gesture interaction and virtual surgical instrument manipulation of the digital manikin. This system uses medical image processing and holographic reality technology to help surgeons perform preoperative surgical planning and intraoperative real-time information guidance to obtain accurate and safe surgical results.
Visual Servoing Using Cosine Similarity Metric

This article presents a new visual servoing method based on cosine similarity metric, which focuses on utilizing cosine distance defined by cosine similarity as the optimization objective of histogram-based direct visual servoing (HDVS) to design the servoing control law. As a more compact global descriptor, the histogram makes direct visual servoing more robust against noise than directly using image intensity. Cosine similarity is the cosine value between two vectors, which has been widely employed to calculate the similarity between multidimensional information. The cosine distance derived from the cosine similarity is more sensitive to the directional difference between the histograms, making the proposed method have a larger convergence rate than the existing Matusita distance-based servoing method. This advantage is verified by simulations, and experiments are conducted on a manipulator to further verify the effectiveness of the proposed method in practical situations.
A Joint Tracking System: Robot is Online to Access Surveillance Views

The application of robots in social life, equipped with sensors and actuators and embedded with AI, assists people in all aspects. However, the first perspective of the robot horizon is heavily constrained, which weakens its performance. A joint tracking system is designed and built to deal with this, by integrating a surveillance system with the robot visual, providing a third perspective. This system takes one horizontal view and two top views from various directions as inputs and matches a person among the frames and in time sequence. In order to deal with the identity match with a huge visual feature gap, a special dataset is collected, simultaneously labeling identities from a mobile robot perspective and multiple indoor static surveillance monitors. The experiment shows that such match is a task worth exploring that can be better handled by training on our dataset than existing open source Re-identification (Re-id) datasets. Moreover, in the real scenario, this system improves the performance on issues like in and out of the robot’s field of vision and heavy occlusion by people or objects.
Free Performance Boost for Voting-based 3D Object Detection in Point Clouds

Recently, detecting objects from 3D point clouds based on the voting mechanism has received increasing attention. Various improvements such as RGB feature embedding, context information fusion and bounding box refinement have been developed to further boost the performance of voting-based models since VoteNet was proposed. In this work, we revisit the VoteNet model and point out the significant negative impact brought by votes from background, which has never been discussed properly in previous research as far as we know. In order to suppress votes from the background to obtain purer vote clusters, we redesign the loss function for the training of voting module of voting-based 3D detector. Experiments on the challenging SUN RGB-D dataset show that the new loss function can significantly boost the detection performance of existing state-of-the-art voting-based 3D detectors with no additional computational overhead.
Towards a Typology of Interdisciplinarity in Cybersecurity: Trade, Choice, and Agnostic-Antagonist

Cybersecurity research increasingly involves non-engineering disciplines, such as psychology, social science and law [41]. In this paper, we argue that cybersecurity research is not only reshaped through new methods and concepts of these adjacent fields, but also through shared interdisciplinary practices. Existing literature on interdisciplinarity in cybersecurity is primarily concerned with defining ideal models that are based on ideals, rather than in empirical research of how interdisciplinarity is formed in practice. We offer an ethnographic analysis of interdisciplinary formats based on our four-year participation in the ongoing interdisciplinary cybersecurity PhD programme SecHuman at the Ruhr-University Bochum, Germany. The PhD programme brings together engineers, social scientists as well as humanities scholars. Drawing on methods and literature of ethnographic science and technology studies (STS), we attend to eight different interdisciplinary formats and analyse how they shape cybersecurity research: its logics of accountability, of innovation, and of ontology [3]. This leads to a typology of five modes of interdisciplinarity that can be found in the PhD programme: 1. choice, 2. subordinate-service, 3. integrative-synthetic, 4. trading, and 5. agonistic-antagonistic. Based on our empirical findings, we discuss how each mode shapes cybersecurity, and conclude with suggestions of how to craft interdisciplinary formats in the field.