Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Advanced Research in Computer Science and Software Engineering

— In the traveling salesman problem (TSP) we wish to find a tour of all nodes in a weighted graph so that the total weight is minimized. The traveling salesman problem is NP-hard but has many real world applications so a good solution would be useful. In this paper, we present several modern optimization techniques to find the shortest tour through all cities (nodes). Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria Foraging Optimization (BFO), and Bee Colony Optimization (BCO) are applied on several datasets of TSP with different number of cities and different representation: distances between cities, or coordinates of cities. Each optimization technique has unique behaviors which survives it against other techniques. In this paper, the results and comparative study will present for each dataset to calculate the minimum distance and plat the resultant path.
Studi e Saggi Linguistici

This paper presents the structure of the LiLa Knowledge Base, i.e. a collection of multi-farious linguistic resources for Latin described with the same vocabulary of knowledge description and interlinked according to the principles of the so-called Linked Data paradigm. Following its highly lexically based nature, the core of the LiLa Knowledge Base consists of a large collection of Latin lemmas, serving as the backbone to achieve interoperability between the resources, by linking all those entries in lexical resources and tokens in corpora that point to the same lemma. After detailing the architecture supporting LiLa , the paper particularly focusses on how we approach the challenges raised by harmonizing different strategies of lemmatization that can be found in linguistic resources for Latin. As an example of the process to connect a linguistic resource to LiLa , the inclusion in the Knowledge Base of a dependency treebank is described and evaluated.
OpenFunction: An extensible data plane abstraction protocol for platform-independent software-defined middleboxes

We propose OpenFunction, an extensible data plane abstraction protocol for platform-independent software-defined middleboxes. The main challenge is how to abstract packet operations, flow states and event generations with elements. The key decision of OpenFunction is: actions/states/events operations should be defined in a uniform pattern and independent from each other. We implemented a working SDM system including one OpenFunction controller and OpenFunction boxes based on Netmap, DPDK and FPGA to verify OpenFunction abstraction.
On Transductive Support Vector Machines

Transductive support vector machines (TSVM) has been widely used as a means of treating partially labeled data in semi-supervised learning. Around it, there has been mystery because of lack of understanding its foundation in generalization. This article aims to clarify several controversial aspects regarding TSVM. Two main results are established. First, TSVM performs no worse than its supervised counterpart SVM when tuning is performed, which is contrary to several studies indicating otherwise. The “alleged” inferior performance of TSVM is mainly because it was not tuned in the process, in addition to the involved minimization routines. Second, we utilize diﬀerence convex programming to derive a nonconvex minimization routine for TSVM, which compares favorably against some state-of-the-art methods. This, together with our learning theory lands some support to TSVM.
Hybrid Control for Multi-Agent Systems in Complex Sensing Environments

Abstract : The overall program provided systematic and computationally responsive research aimed at control of multi-agent systems, and more generally systems that have both discrete and continuous dynamics, using convex optimization and semidefinite programming as the foundation. The program has produced research results in several domains. In the area of distributed control several results were obtained: new stabilization methods were developed for systems in which dynamical agents interact over bandlimited channels; synthesis methods were also developed for metric-based performance optimization of distributed systems over graphs. Systems with simultaneously network latency and finite precision sensing were targeted and algorithms were developed that can guarantee stabilization in these limited information scenarios. For switched systems a separate program accomplishment was the development of equivalence and separation principles for joint synthesis of switching rules and policies. Also, the first known exact convex conditions for receding horizon control of switched systems were found, together with design algorithms for optimizing metric-based performance. New decidability and verification results for general hybrid setting were developed.
SocialNLP 2020 The Eighth International Workshop on Natural Language Processing for Social Media Proceedings of the Conference

Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information. Fairclough and Wodak (1997) discuss presupposition at a discourse level where some implicit claims are taken for granted in the explicit meaning of a text or utterance. From the Gricean perspective, the presuppositions of a sentence determine the class of contexts in which the sentence could be felicitously uttered. This paper aims to correlate the type of knowledge presupposed in a news article to the bias present in it. We propose a set of guidelines to identify various kinds of presuppositions in news articles and present a dataset consisting of 1050 articles which are annotated for bias (positive, negative or neutral) and the magnitude of presupposition. We introduce a supervised classification approach for detecting bias in political news which significantly outperforms the existing systems.
Learned − Weights BLP − Manual − Weights MLN − Learned − Weights MLN − Manual − Weights

Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires “reading between the lines”. Human readers naturally use common sense knowledge to infer such implicit information from the explicitly stated facts. We propose an approach that uses Bayesian Logic Programs (BLPs), a statistical relational model combining firstorder logic and Bayesian networks, to infer additional implicit information from extracted facts. It involves learning uncertain commonsense knowledge (in the form of probabilistic first-order rules) from natural language text by mining a large corpus of automatically extracted facts. These rules are then used to derive additional facts from extracted information using BLP inference. Experimental evaluation on a benchmark data set for machine reading demonstrates the efficacy of our approach.
Internet Engineering Task Force Audio/video Transport Wg Internet-draft Issues with Rtp Sampling

Status of this Memo This document is an Internet-Draft. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as \work in progress". To learn the current status of any Internet-Draft, please check the \1id-abstracts.txt" listing contained in the Internet-Drafts Shadow Directories on ftp.is. co.za (Africa), nic.nordu. net (Europe), 1 Abstract In order to control the ow of RTCP packets in large multicast groups, session participants are required to transmit their packets with a period proportional to the group size. Obtaining a group size estimate generally requires a participant to maintain a list of group members. As this can require signiicant memory, particularly for embedded systems, RTP has been revised to allow for a statistical sampling procedure which allows the memory size to be bounded for all group sizes. However, this sampling algorithm can interact with other aspects of RTP. In particular, we have identiied three problems. First, RTP sampling has an adverse aaect on the performance of BYE reconsideration. Secondly, it can cause inaccurate estimates with dynamic group sizes that decrease rapidly. Thirdly, the current SSRC sampling algorithm grossly overestimates the group size when there are a few senders. We discuss these problems in detail and present simple solutions. 2 Introduction In order to control the ow of RTCP packets in large multicast groups, session participants are required to transmit their packets with a period proportional to the group size. Obtaining a group size estimate generally requires a participant to maintain a list of group members. As this can require signiicant memory, particularly for embedded systems, RTP 1] has been revised to allow for a statistical sampling procedure which allows the memory size to be bounded for all group sizes. This algorithm operates by applying an SSRC mask with m bits equal to one to each packet received. Initially, m starts at 0. If the bitwise AND of the SSRC of the incoming packet, and the mask, equals the bitwise AND of some key and the mask, the packet is accepted and the SSRC counted in the group size estimate. When the memory requirements …
The Little Engine(s) That Could: Scaling Online Social Networks

The difficulty of partitioning social graphs has introduced new system design challenges for scaling of online social networks (OSNs). Vertical scaling by resorting to full replication can be a costly proposition. Scaling horizontally by partitioning and distributing data among multiple servers using, for e.g., distributed hash tables (DHTs), can suffer from expensive interserver communication. Such challenges have often caused costly rearchitecting efforts for popular OSNs like Twitter and Facebook. We design, implement, and evaluate SPAR, a Social Partitioning and Replication middleware that mediates transparently between the application and the database layer of an OSN. SPAR leverages the underlying social graph structure in order to minimize the required replication overhead for ensuring that users have their neighbors' data colocated in the same machine. The gains from this are multifold: Application developers can assume local semantics, i.e., develop as they would for a single machine; scalability is achieved by adding commodity machines with low memory and network I/O requirements; and N+K redundancy is achieved at a fraction of the cost. We provide a complete system design, extensive evaluation based on datasets from Twitter, Orkut, and Facebook, and a working implementation. We show that SPAR incurs minimum overhead, can help a well-known Twitter clone reach Twitter's scale without changing a line of its application logic, and achieves higher throughput than Cassandra, a popular key-value store database.
Finite-time fuzzy stabilisation and control for nonlinear descriptor systems & break ; with non-zero initial state

Finite-time fuzzy stabilisation and control for nonlinear descriptor systems&break; with non-zero initial state Zhan Su a b , Qingling Zhang a , Jun Ai b & Xin Sun a c a Institute of Systems Science, Northeastern University, Liaoning, China b College of Information Science and Engineering, Northeastern University, Liaoning, China c Department of Computer and Mathematics Teaching, Shenyang Normal University, Liaoning, China Published online: 02 Apr 2013.
Point Cloud Registration Method for Pipeline Workpieces Based On NDT and Improved ICP Algorithms

A new point cloud registration method based on NDT and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, according to the FPFH algorithm, the feature points of point clouds are extracted to reduce the number of point clouds. Then, the normal distribution transformation (NDT) is used to achieve rough registration of point clouds, so that the two point clouds have relatively good initial position and posture. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the NDT+ICP algorithm are tested respectively, and the results show that the proposed algorithm is faster, with lower error and fewer iterations, which provides a reference for similar problems.
DanceAnyWay: Synthesizing Beat-Guided 3D Dances with Randomized Temporal Contrastive Learning

We present DanceAnyWay, a generative learning method to synthesize beat-guided dances of 3D human characters synchronized with music. Our method learns to disentangle the dance movements at the beat frames from the dance movements at all the remaining frames by operating at two hierarchical levels. At the coarser "beat" level, it encodes the rhythm, pitch, and melody information of the input music via dedicated feature representations only at the beat frames. It leverages them to synthesize the beat poses of the target dances using a sequence-to-sequence learning framework. At the finer "repletion" level, our method encodes similar rhythm, pitch, and melody information from all the frames of the input music via dedicated feature representations. It generates the full dance sequences by combining the synthesized beat and repletion poses and enforcing plausibility through an adversarial learning framework. Our training paradigm also enforces fine-grained diversity in the synthesized dances through a randomized temporal contrastive loss, which ensures different segments of the dance sequences have different movements and avoids motion freezing or collapsing to repetitive movements. We evaluate the performance of our approach through extensive experiments on the benchmark AIST++ dataset and observe improvements of about 7%-12% in motion quality metrics and 1.5%-4% in motion diversity metrics over the current baselines, respectively. We also conducted a user study to evaluate the visual quality of our synthesized dances. We noted that, on average, the samples generated by our method were about 9-48% more preferred by the participants and had a 4-27% better five-point Likert-scale score over the best available current baseline in terms of motion quality and synchronization. Our source code and project page are available at https://github.com/aneeshbhattacharya/DanceAnyWay.
Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning

To support various applications, business owners often seek the customized models that are obtained by fine-tuning a pre-trained LLM through the API provided by LLM owners or cloud servers. However, this process carries a substantial risk of model misuse, potentially resulting in severe economic consequences for business owners. Thus, safeguarding the copyright of these customized models during LLM fine-tuning has become an urgent practical requirement, but there are limited existing solutions to provide such protection. To tackle this pressing issue, we propose a novel watermarking approach named"Double-I watermark". Specifically, based on the instruct-tuning data, two types of backdoor data paradigms are introduced with trigger in the instruction and the input, respectively. By leveraging LLM's learning capability to incorporate customized backdoor samples into the dataset, the proposed approach effectively injects specific watermarking information into the customized model during fine-tuning, which makes it easy to inject and verify watermarks in commercial scenarios. We evaluate the proposed"Double-I watermark"under various fine-tuning methods, demonstrating its harmlessness, robustness, uniqueness, imperceptibility, and validity through both theoretical analysis and experimental verification.
Counterfactual Generation with Identifiability Guarantees

Counterfactual generation lies at the core of various machine learning tasks, including image translation and controllable text generation. This generation process usually requires the identification of the disentangled latent representations, such as content and style, that underlie the observed data. However, it becomes more challenging when faced with a scarcity of paired data and labeling information. Existing disentangled methods crucially rely on oversimplified assumptions, such as assuming independent content and style variables, to identify the latent variables, even though such assumptions may not hold for complex data distributions. For instance, food reviews tend to involve words like tasty, whereas movie reviews commonly contain words such as thrilling for the same positive sentiment. This problem is exacerbated when data are sampled from multiple domains since the dependence between content and style may vary significantly over domains. In this work, we tackle the domain-varying dependence between the content and the style variables inherent in the counterfactual generation task. We provide identification guarantees for such latent-variable models by leveraging the relative sparsity of the influences from different latent variables. Our theoretical insights enable the development of a doMain AdapTive counTerfactual gEneration model, called (MATTE). Our theoretically grounded framework achieves state-of-the-art performance in unsupervised style transfer tasks, where neither paired data nor style labels are utilized, across four large-scale datasets. Code is available at https://github.com/hanqi-qi/Matte.git
LLMBind: A Unified Modality-Task Integration Framework

While recent progress in multimodal large language models tackles various modality tasks, they posses limited integration capabilities for complex multi-modality tasks, consequently constraining the development of the field. In this work, we take the initiative to explore and propose the LLMBind, a unified framework for modality task integration, which binds Large Language Models and corresponding pre-trained task models with task-specific tokens. Consequently, LLMBind can interpret inputs and produce outputs in versatile combinations of image, text, video, and audio. Specifically, we introduce a Mixture-of-Experts technique to enable effective learning for different multimodal tasks through collaboration among diverse experts. Furthermore, we create a multi-task dataset comprising 400k instruction data, which unlocks the ability for interactive visual generation and editing tasks. Extensive experiments show the effectiveness of our framework across various tasks, including image, video, audio generation, image segmentation, and image editing. More encouragingly, our framework can be easily extended to other modality tasks, showcasing the promising potential of creating a unified AI agent for modeling universal modalities.
Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject.
Characterizing BV- and BD-ellipticity for a class of positively 1-homogeneous surface energy densities

Lower semicontinuity of surface energies in integral form is known to be equivalent to BV-ellipticity of the surface density. In this paper, we prove that BV-ellipticity coincides with the simpler notion of biconvexity for a class of densities that depend only on the jump height and jump normal, and are positively 1-homogeneous in the first argument. The second main result is the analogous statement in the setting of bounded deformations, where we show that BD-ellipticity reduces to symmetric biconvexity. Our techniques are primarily inspired by constructions from the analysis of structured deformations and the general theory of free discontinuity problems.
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning

Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approach the sample-efficiency challenge by expanding the information collected per query to contain both preferences and optional text prompting. To accomplish this, we leverage the zero-shot capabilities of a large language model (LLM) to reason from the text provided by humans. To accommodate the additional query information, we reformulate the reward learning objectives to contain flexible highlights -- state-action pairs that contain relatively high information and are related to the features processed in a zero-shot fashion from a pretrained LLM. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications. Additionally, the collective feedback collected serves to train a robot on socially compliant trajectories in a simulated social navigation landscape. We provide video examples of the trained policies at https://sites.google. com/view/rl-predilect
Efficient Unbiased Sparsification

An unbiased $m$-sparsification of a vector $p\in \mathbb{R}^n$ is a random vector $Q\in \mathbb{R}^n$ with mean $p$ that has at most $m<n$ nonzero coordinates. Unbiased sparsification compresses the original vector without introducing bias; it arises in various contexts, such as in federated learning and sampling sparse probability distributions. Ideally, unbiased sparsification should also minimize the expected value of a divergence function $\mathsf{Div}(Q,p)$ that measures how far away $Q$ is from the original $p$. If $Q$ is optimal in this sense, then we call it efficient. Our main results describe efficient unbiased sparsifications for divergences that are either permutation-invariant or additively separable. Surprisingly, the characterization for permutation-invariant divergences is robust to the choice of divergence function, in the sense that our class of optimal $Q$ for squared Euclidean distance coincides with our class of optimal $Q$ for Kullback-Leibler divergence, or indeed any of a wide variety of divergences.