OpenRaiser/Intern-Atlas huggingface dataset:
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition
Venue: arXiv cs.CL 2022
Year: 2022

Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.
Performance evaluation of switching between WiFi and LiFi under a common virtual network interface
Venue: arXiv cs.NI 2022
Year: 2022

We consider a hybrid wireless local area network composed of both WiFi and LiFi Access Points (AP) and wireless devices. Each device is identified in the network by a unique IP address, using a virtual network interface obtained by bonding the WiFi and LiFi physical interfaces, implemented through commercially available products. We measure the time it takes to switch between the two physical interfaces and its impact on the traffic flow, under different settings of the mechanisms used by the interface bonding driver. Different specific triggering events are considered for the switch, namely: an (simulated) interface malfunctioning or unintended shutdown, a signal loss, and a manual (intended) switch. Our experimental results show that the different types of triggering events have an impact on the time it takes to reconfigure the currently active physical interface (which is used by the virtual interface to send/receive data), with connection recovery times ranging from few tens milliseconds to few seconds. This entails a packet loss on active flows which, in the worst case, we quantify in a maximum loss of up to 1% of the traffic flowing during 1 second.
$p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
Venue: arXiv cs.DS 2022
Year: 2022

We study the $p$-generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard normal cdf, by a $p$-generalized normal distribution for $p\in[1, \infty)$. The $p$-generalized normal distributions \citep{Sub23} are of special interest in statistical modeling because they fit much more flexibly to data. Their tail behavior can be controlled by choice of the parameter $p$, which influences the model's sensitivity to outliers. Special cases include the Laplace, the Gaussian, and the uniform distributions. We further show how the maximum likelihood estimator for $p$-generalized probit regression can be approximated efficiently up to a factor of $(1+\varepsilon)$ on large data by combining sketching techniques with importance subsampling to obtain a small data summary called coreset.
Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The Impact of Sample Sizes
Venue: arXiv cs.DC 2022
Year: 2022

Modern computing systems are increasingly more complex, with their multicore CPUs and GPUs accelerators changing yearly, if not more often. It thus has become very challenging to write programs that efficiently use the associated complex memory systems and take advantage of the available parallelism. Autotuning addresses this by optimizing parameterized code to the targeted hardware by searching for the optimal set of parameters. Empirical autotuning has therefore gained interest during the past decades. While new autotuning algorithms are regularly presented and published, we will show why comparing these autotuning algorithms is a deceptively difficult task. In this paper, we describe our empirical study of state-of-the-art search techniques for autotuning by comparing them on a range of sample sizes, benchmarks and architectures. We optimize 6 tunable parameters with a search-space size of over 2 million. The algorithms studied include Random Search (RS), Random Forest Regression (RF), Genetic Algorithms (GA), Bayesian Optimization with Gaussian Processes (BO GP) and Bayesian Optimization with Tree-Parzen Estimators (BO TPE). Our results on the ImageCL benchmark suite suggest that the ideal autotuning algorithm heavily depends on the sample size. In our study, BO GP and BO TPE outperform the other algorithms in most scenarios with sample sizes from 25 to 100. However, GA usually outperforms the others for sample sizes 200 and beyond. We generally see the most speedup to be gained over RS in the lower range of sample sizes (25-100). However, the algorithms more consistently outperform RS for higher sample sizes (200-400). Hence, no single state-of-the-art algorithm outperforms the rest for all sample sizes. Some suggestions for future work are also included.
Fast Hybrid Image Retargeting
Venue: arXiv cs.CV 2022
Year: 2022

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping distortions with the use of content-aware cropping. The pipeline of the proposed approach consists of the following steps. First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models. Then, a preliminary warping mesh is computed using axis aligned deformations, enhanced with the use of a distortion measure to ensure low warping deformations. Finally, the retargeted image is produced using a content-aware cropping algorithm. In order to evaluate our method, we perform a user study based on the RetargetMe benchmark. Experimental analyses show that our method outperforms recent approaches, while running in a fraction of their execution time.
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
Venue: arXiv cs.CL 2022
Year: 2022

The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this demonstration we introduce a new workflow where the analyst directly verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. We present the design and implementation of a toolkit with a user interface, as well as experiments on four IE tasks that show that the system achieves very good performance at zero-shot learning using only 5--15 minutes per type of a user's effort. Our demonstration system is open-sourced at https://github.com/BBN-E/ZS4IE . A demonstration video is available at https://vimeo.com/676138340 .
The best defense is a good defense: adapting negotiation methods for tackling pressure over software project estimates
Venue: arXiv cs.SE 2022
Year: 2022

Software estimation is critical for a software project's success and a challenging activity. We argue that estimation problems are not restricted to the generation of estimates but also their use for commitment establishment: project stakeholders pressure estimators to change their estimates or to accept unrealistic commitments to attain business goals. In this study, we employed a Design Science Research (DSR) methodology to design an artifact based on negotiation methods, to empower software estimators in defending their estimates and searching for alternatives to unrealistic commitments when facing pressure. The artifact is a concrete step towards disseminating the soft skill of negotiation among practitioners. We present the preliminary results from a focus group that showed that practitioners from the software industry could use the artifact in a concrete scenario when estimating and establishing commitments about a software project. Our future steps include improving the artifact with the suggestions from focus group participants and evaluating it empirically in real software projects in the industry.
Rank-based Non-dominated Sorting
Venue: arXiv cs.NE 2022
Year: 2022

Non-dominated sorting is a computational bottleneck in Pareto-based multi-objective evolutionary algorithms (MOEAs) due to the runtime-intensive comparison operations involved in establishing dominance relationships between solution candidates. In this paper we introduce Rank Sort, a non-dominated sorting approach exploiting sorting stability and ordinal information to avoid expensive dominance comparisons in the rank assignment phase. Two algorithmic variants are proposed: the first one, RankOrdinal (RO), uses ordinal rank comparisons in order to determine dominance and requires O(N) space; the second one, RankIntersect (RS), uses set intersections and bit-level parallelism and requires O(N^2) space. We demonstrate the efficiency of the proposed methods in comparison with other state of the art algorithms in empirical simulations using the NSGA2 algorithm as well as synthetic benchmarks. The RankIntersect algorithm is able to significantly outperform the current state of the art offering up to 30% speed-up for many objectives. C++ implementations are provided for all algorithms.
UKP-SQUARE: An Online Platform for Question Answering Research
Venue: arXiv cs.CL 2022
Year: 2022

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., generative, discriminative), and setups (e.g., with or without retrieval). Despite having a large number of powerful, specialized QA pipelines (which we refer to as Skills) that consider a single domain, model or setup, there exists no framework where users can easily explore and compare such pipelines and can extend them according to their needs. To address this issue, we present UKP-SQUARE, an extensible online QA platform for researchers which allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. In addition, QA researchers can develop, manage, and share their custom Skills using our microservices that support a wide range of models (Transformers, Adapters, ONNX), datastores and retrieval techniques (e.g., sparse and dense). UKP-SQUARE is available on https://square.ukp-lab.de.
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
Venue: arXiv cs.CV 2022
Year: 2022

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system which has multiple contributions including a random batch selection mechanism to reduce inter-batch correlation and a normalcy suppression block which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. Extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate a superior anomaly detection capability of our approach.
Code Smells for Machine Learning Applications
Venue: arXiv cs.SE 2022
Year: 2022

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. In particular, code smells have rarely been studied in this domain. Although machine learning code is usually integrated as a small part of an overarching system, it usually plays an important role in its core functionality. Hence ensuring code quality is quintessential to avoid issues in the long run. This paper proposes and identifies a list of 22 machine learning-specific code smells collected from various sources, including papers, grey literature, GitHub commits, and Stack Overflow posts. We pinpoint each smell with a description of its context, potential issues in the long run, and proposed solutions. In addition, we link them to their respective pipeline stage and the evidence from both academic and grey literature. The code smell catalog helps data scientists and developers produce and maintain high-quality machine learning application code.
Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments
Venue: arXiv cs.RO 2022
Year: 2022

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful urban deployment, particularly in emergency landing situations. Nonetheless, validating autonomous landing in real scenarios is a challenging task involving a high risk of injuring people. In this work, we propose a framework for real-time safe and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments. We propose to use the Unreal graphics engine coupled with the AirSim plugin for drone's simulation, and evaluate autonomous landing strategies based on visual detection of Safe Landing Zones (SLZ) in populated scenarios. Then, we study two different criteria for selecting the "best" SLZ, and evaluate them during autonomous landing of a virtual drone in different scenarios and conditions, under different distributions of people in urban scenes, including moving people. We evaluate different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works in this challenging task, and analyze them through an important number of randomized iterations. The study suggests that the use of the autonomous landing algorithms considerably helps to prevent accidents involving humans, which may allow to unleash the full potential of drones in urban environments near to people.
Opportunistic Qualitative Planning in Stochastic Systems with Preferences over Temporal Logic Objectives
Venue: arXiv cs.FL 2022
Year: 2022

Preferences play a key role in determining what goals/constraints to satisfy when not all constraints can be satisfied simultaneously. In this work, we study preference-based planning in a stochastic system modeled as a Markov decision process, subject to a possible incomplete preference over temporally extended goals. Our contributions are three folds: First, we introduce a preference language to specify preferences over temporally extended goals. Second, we define a novel automata-theoretic model to represent the preorder induced by given preference relation. The automata representation of preferences enables us to develop a preference-based planning algorithm for stochastic systems. Finally, we show how to synthesize opportunistic strategies that achieves an outcome that improves upon the current satisfiable outcome, with positive probability or with probability one, in a stochastic system. We illustrate our solution approaches using a robot motion planning example.
Quantifying Demonstration Quality for Robot Learning and Generalization
Venue: arXiv cs.RO 2022
Year: 2022

Learning from Demonstration (LfD) seeks to democratize robotics by enabling diverse end-users to teach robots to perform a task by providing demonstrations. However, most LfD techniques assume users provide optimal demonstrations. This is not always the case in real applications where users are likely to provide demonstrations of varying quality, that may change with expertise and other factors. Demonstration quality plays a crucial role in robot learning and generalization. Hence, it is important to quantify the quality of the provided demonstrations before using them for robot learning. In this paper, we propose quantifying the quality of the demonstrations based on how well they perform in the learned task. We hypothesize that task performance can give an indication of the generalization performance on similar tasks. The proposed approach is validated in a user study (N = 27). Users with different robotics expertise levels were recruited to teach a PR2 robot a generic task (pressing a button) under different task constraints. They taught the robot in two sessions on two different days to capture their teaching behaviour across sessions. The task performance was utilized to classify the provided demonstrations into high-quality and low-quality sets. The results show a significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the task performance and generalization performance across all participants. We also found that users clustered into two groups: Users who provided high-quality demonstrations from the first session, assigned to the fast-adapters group, and users who provided low-quality demonstrations in the first session and then improved with practice, assigned to the slow-adapters group. These results highlight the importance of quantifying demonstration quality, which can be indicative of the adaptation level of the user to the task.
Evaluation of the impacts of decomposing a monolithic application into microservices: A case study
Venue: arXiv cs.SE 2022
Year: 2022

Monolithic applications are being decomposed to a microservices architecture, aiming to improve maintainability, performance and modularization. Although such decompositions have now been widely carried out in the industry, little is reported in the literature about the impacts of these decompositions. This work, therefore, reports a case study carried out to investigate the impacts of the decomposition of a real industry application for microservices architecture. The target application of the study refers to a raid operation, performed by a financial institution, which was extracted from a monolithic application to an application based on microservices. In particular, metrics were applied to monolithic and microservice-based applications, aiming to quantify coupling, cohesion, CPU consumption and memory consumption. The results obtained show that the microservices architecture generated better results for the software modularity metrics, in addition to lower memory and CPU consumption. Finally, this work brings insight and points out challenges and future directions of research that need to be explored by academia and industry.
Intermunicipal Travel Networks of Mexico (2020-2021)
Venue: arXiv cs.SI 2022
Year: 2022

We present a collection of networks that describe the travel patterns between municipalities in Mexico between 2020 and 2021. Using anonymized mobile device geo-location data we constructed directed, weighted networks representing the (normalized) volume of travels between municipalities. We analysed changes in global (graph total weight sum), local (centrality measures), and mesoscale (community structure) network features. We observe that changes in these features are associated with factors such as Covid-19 restrictions and population size. In general, events in early 2020 (when initial Covid-19 restrictions were implemented) induced more intense changes in network features, whereas later events had a less notable impact in network features. We believe these networks will be useful for researchers and decision makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
Venue: arXiv cs.AR 2022
Year: 2022

In view of the performance limitations of fully-decoupled designs for neural architectures and accelerators, hardware-software co-design has been emerging to fully reap the benefits of flexible design spaces and optimize neural network performance. Nonetheless, such co-design also enlarges the total search space to practically infinity and presents substantial challenges. While the prior studies have been focusing on improving the search efficiency (e.g., via reinforcement learning), they commonly rely on co-searches over the entire architecture-accelerator design space. In this paper, we propose a \emph{semi}-decoupled approach to reduce the size of the total design space by orders of magnitude, yet without losing optimality. We first perform neural architecture search to obtain a small set of optimal architectures for one accelerator candidate. Importantly, this is also the set of (close-to-)optimal architectures for other accelerator designs based on the property that neural architectures' ranking orders in terms of inference latency and energy consumption on different accelerator designs are highly similar. Then, instead of considering all the possible architectures, we optimize the accelerator design only in combination with this small set of architectures, thus significantly reducing the total search cost. We validate our approach by conducting experiments on various architecture spaces for accelerator designs with different dataflows. Our results highlight that we can obtain the optimal design by only navigating over the reduced search space. The source code of this work is at \url{https://github.com/Ren-Research/CoDesign}.
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Venue: arXiv cs.CL 2022
Year: 2022

Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect. In this work, we propose to use information that can be automatically extracted from the next user utterance, such as its sentiment or whether the user explicitly ends the conversation, as a proxy to measure the quality of the previous system response. This allows us to train on a massive set of dialogs with weak supervision, without requiring manual system turn quality annotations. Experiments show that our model is comparable to models trained on human annotated data. Furthermore, our model generalizes across both spoken and written open-domain dialog corpora collected from real and paid users.