Reviewerly/exHarmony huggingface dataset:
The effect of different information sources on prosodic boundary perception

This study aims to quantify the effect of several information sources: acoustic, higher-level linguistic, and knowledge of the prosodic system of the language, on the perception of prosodic boundaries. An experiment with native and non-native participants investigating the identification of prosodic boundaries in Japanese was conducted. It revealed that non-native speakers as well as native speakers with access only to acoustic information can recognize boundaries better than chance level. However, knowledge of both the prosodic system and of higher-level information are required for a good boundary identification, each one having similar or higher importance than that of acoustic information.
Blockchain-based Trust Information Storage in Crowdsourced IoT Services

We propose a novel distributed integrity-preserving framework for storing trust information in crowdsourced IoT environments. The integrity and availability of the trust information is paramount to ensure accurate trust assessment. Our proposed framework leverages the blockchain to build a distributed storage medium for trust-related information that ensures its integrity. We propose a geo-scoping approach, which ensures that trust-related information is only available where needed, thus, enabling fast access and storage space preservation. We conduct several experiments using real datasets to highlight the effectiveness of our framework.
Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language

We present results of a project on emotion classification on historical German plays of Enlightenment, Storm and Stress, and German Classicism.We have developed a hierarchical annotation scheme consisting of 13 subemotions like suffering, love and joy that sum up to 6 main and 2 polarity classes (positive/negative).We have conducted textual annotations on 11 German plays and have acquired over 13,000 emotion annotations by two annotators per play.We have evaluated multiple traditional machine learning approaches as well as transformer-based models pretrained on historical and contemporary language for a single-label text sequence emotion classification for the different emotion categories.The evaluation is carried out on three different instances of the corpus: (1) taking all annotations, (2) filtering overlapping annotations by annotators, (3) applying a heuristic for speech-based analysis.Best results are achieved on the filtered corpus with the best models being large transformer-based models pretrained on contemporary German language.For the polarity classification accuracies of up to 90% are achieved.The accuracies become lower for settings with a higher number of classes, achieving 66% for 13 sub-emotions.Further pretraining of a historical model with a corpus of dramatic texts led to no improvements.
Weakly-supervised deep learning for breast tumor segmentation in ultrasound images

Breast tumor segmentation is a preliminary step for breast tumor diagnosis. It is expensive to obtain pixel-level labels for breast tumor segmentation. We aim to perform breast tumor segmentation in breast ultrasound (BUS) images with image-level labels. To this end, we propose a class activation mapping with level set (CAM-LS) method. Given a training dataset consisting of BUS images with or without breast tumors which naturally contain image-level labels, a classification network is trained for breast tumor recognition. Class activation maps (CAMs) are generated for positive predictions, which highlight discriminative regions of breast tumors. Anatomical constraints are used to reduce the search space for breast tumors. For breast tumor recognition, the proposed method achieves sensitivity of 92.0%, precision of 92.8%, specificity of 95.2%, accuracy of 93.9% and F1-score of 0.924. For breast tumor segmentation, the proposed method obtains Dice similarity coefficient (DSC) of 73.5 ± 18.0% and interaction-over-union (IoU) of 60.8 ± 19.7%. The proposed CAM-LS method performs automated breast tumor segmentation with image-level labels only and is demonstrated in the experiments to have good generalization on different ultrasound platforms.
Environmental parameter estimation with the two-level atom probes

A novel scheme is proposed to estimate three environmental parameters, the detuning, the temperature and the squeezing strength with one-qubit or two-qubit probes. Quantum Fisher information and the fidelity of the atom probes are calculated. When the detuning between the frequency of cavity field and the atomic transition frequency is estimated, the dynamics of quantum Fisher information shows oscillatory and rising behaviors. To estimate the temperature of the thermal reservoir, the one-qubit probe with the superposition initial state is more favorable than the two-qubit probe with the entangled initial state. When the squeezing strength of the squeezed vacuum reservoir is estimated, we find that the estimation precision is significantly improved by utilizing the two-qubit probe with the maximal entangled initial state. Our work provides a potential application in the open quantum system and quantum information processing.
Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks

Identifying and understanding underlying sentiments or emotions in text is a key component of multiple natural language processing applications. While simple polarity sentiment analysis is a well-studied subject, fewer advances have been made in identifying more complex, finer-grained emotions using only textual data. In this paper, we present a Transformer-based model with a Fusion of Adapter layers that leverages knowledge from more simple sentiment analysis tasks to improve the emotion detection task on large scale datasets, such as CMU-MOSEI, using the textual modality only. Results show that our proposed method is competitive with other approaches. Experiments on the CMU-MOSEI, Emotions and GoEmotions datasets show that the knowledge from the sentiment analysis task can indeed be leveraged for emotion recognition.
A picture is worth a thousand words: visual thinking between creative thinking and critical thinking in the teaching-learning processes

Although contemporary society has a predominantly visual character, education still privileges the written word over all other forms of communication and subordinates the visual text to that written in understanding. In fact, teachers are not always well prepared to analyze the visual language and to discuss its meanings, while working on precise goals and curricular contents, relying mainly on verbal and printed language. Indeed, effective communication extends beyond the ability to use verbal language to include communication through visual arts and expressive movement, which highlights the value assumed by the use of visual thinking in the use of media and technologies to convey messages and the importance of understanding how to judge their effectiveness, as well as assess their impact.
MERLOT: Multimodal Neural Script Knowledge Models

As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future. We introduce MERLOT, a model that learns multimodal script knowledge by watching millions of YouTube videos with transcribed speech -- in an entirely label-free, self-supervised manner. By pretraining with a mix of both frame-level (spatial) and video-level (temporal) objectives, our model not only learns to match images to temporally corresponding words, but also to contextualize what is happening globally over time. As a result, MERLOT exhibits strong out-of-the-box representations of temporal commonsense, and achieves state-of-the-art performance on 12 different video QA datasets when finetuned. It also transfers well to the world of static images, allowing models to reason about the dynamic context behind visual scenes. On Visual Commonsense Reasoning, MERLOT answers questions correctly with 80.6% accuracy, outperforming state-of-the-art models of similar size by over 3%, even those that make heavy use of auxiliary supervised data (like object bounding boxes). Ablation analyses demonstrate the complementary importance of: 1) training on videos versus static images; 2) scaling the magnitude and diversity of the pretraining video corpus; and 3) using diverse objectives that encourage full-stack multimodal reasoning, from the recognition to cognition level.
Sketch-Based Creativity Support Tools Using Deep Learning

Sketching is a natural and effective visual communication medium commonly used in creative processes. Recent developments in deep-learning models drastically improved machines’ ability in understanding and generating visual content. An exciting area of development explores deep-learning approaches used to model human sketches, opening opportunities for creative applications. This chapter describes three fundamental steps in developing deep-learning-driven creativity support tools that consume and generate sketches: (1) a data collection effort that generated a new paired dataset between sketches and mobile user interfaces; (2) a sketch-based user interface retrieval system adapted from state-of-the-art computer vision techniques; and, (3) a conversational sketching system that supports the novel interaction of a natural-language-based sketch/critique authoring process. In this chapter, we survey relevant prior work in both the deep-learning and human-computer interaction communities, document the data collection process and the systems’ architectures in detail, present qualitative and quantitative results, and paint the landscape of several future research directions in this exciting area.
Going Car-free: Investigating Mobility Practice Transformations and the Role of ICT

With the debates on climate change and sustainability, a reduction of the share of cars in the modal split has become increasingly prevalent in both public and academic discourse. Besides some motivational approaches, there is a lack of ICT artifacts that successfully raise the ability of consumers to adopt sustainable mobility patterns. To further understand the requirements and the design of these artifacts within everyday mobility adopted a practice-lens. This lens is helpful to get a broader perspective on the use of ICT artifacts along consumers’ transformational journey towards sustainable mobility practices. Based on 12 retrospective interviews with car-free mobility consumers, we argue that artifacts should not be viewed as ’magic-bullet’ solutions but should accompany the complex transformation of practices in multifaceted ways. Moreover, we highlight in particular the difficulties of appropriating shared infrastructures and aligning own practices with them. This opens up a design space to provide more support for these kinds of material-interactions, to provide access to consumption infrastructures and make them usable, rather than leaving consumers alone with increased motivation.
An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction

Ranking-oriented cross-project defect prediction (ROCPDP), which ranks software modules of a new target industrial project based on the predicted defect number or density, has been suggested in the literature. A major concern of ROCPDP is the distribution difference between the source project (aka. within-project) data and target project (aka. cross-project) data, which evidently degrades prediction performance. To investigate the impacts of training data selection methods on the performances of ROCPDP models, we examined the practical effects of nine training data selection methods, including a global filter, which does not filter out any cross-project data. Additionally, the prediction performances of ROCPDP models trained on the filtered cross-project data using the training data selection methods were compared with those of ranking-oriented within-project defect prediction (ROWPDP) models trained on sufficient and limited within-project data. Eleven available defect datasets from the industrial projects were considered and evaluated using two ranking performance measures, i.e., FPA and Norm(Popt). The results showed no statistically significant differences among these nine training data selection methods in terms of FPA and Norm(Popt). The performances of ROCPDP models trained on filtered cross-project data were not comparable with those of ROWPDP models trained on sufficient historical within-project data. However, ROCPDP models trained on filtered cross-project data achieved better performance values than ROWPDP models trained on limited historical within-project data. Therefore, we recommended that software quality teams exploit other project datasets to perform ROCPDP when there is no or limited within-project data.
On Equivalence between Linear-chain Conditional Random Fields and Hidden Markov Chains

Practitioners successfully use hidden Markov chains (HMCs) in different problems for about sixty years. HMCs belong to the family of generative models and they are often compared to discriminative models, like conditional random fields (CRFs). Authors usually consider CRFs as quite different from HMCs, and CRFs are often presented as interesting alternative to HMCs. In some areas, like natural language processing (NLP), discriminative models have completely supplanted generative models. However, some recent results show that both families of models are not so different, and both of them can lead to identical processing power. In this paper we compare the simple linear-chain CRFs to the basic HMCs. We show that HMCs are identical to CRFs in that for each CRF we explicitly construct an HMC having the same posterior distribution. Therefore, HMCs and linear-chain CRFs are not different but just differently parametrized models.
Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence

Artificial intelligence (AI) techniques such as machine learning (ML) and specifically deep learning (DL) has brought significant success to many applications areas such as marketing, computer vision, medical imaging etc. However, the use of these techniques in the wireless communications domain has not been very well explored. In fact, artificial intelligence can play a vital role for communication systems that require a high degree of availability and reliability such as in the field of aeronautical communications for air traffic control. With the ever-growing increase in the air traffic any loss of communication due to jamming can result in devastating effects. For such safety critical communications, the deep learning based intelligent systems can play an important role to support anti-jamming. In this paper, the performance of a deep learning based convolutional neural network for signal modulation classification in safety critical aeronautical communications has been explored as an alternative to traditional methods.
A path planning method of UAV in mountainous environment

In order to promote the application of UAV in remote mountainous areas, this paper proposes a path planning method for UAV in mountainous areas. Firstly, we consider the reference terrain and establish the elliptic cone model of obstacles. The mountain environment model is obtained after the fusion of the two. By considering the horizontal and vertical distances between UAV and obstacles during flight, we calculate the flight influence. Combined with the constraints of flight influence, manoeuvrability and flight environment, a mathematical model was established to optimize the shortest flight path. Finally, the simulation experiment is carried out to solve the model by PSO algorithm, get the UAV path.
Blockchain in Administration

Blockchain denotes a variety of broad technologies to interchange data and handle digital assets in a mesh of distributed network. The primary demand focused in the study is how blockchain technology is helping in the invention and renovation of government processes, such as application in reducing of corruption, and in healthcare, etc. In this paper, the authors present a significant appraisal of blockchain technologies established in related works and consider their suggestions for administrative processes. There is a need to move from a “technology-driven approach” to a “need-driven approach” where blockchain utilization is made to order to safeguard the fit with necessities of managerial methods and in which the managerial method is improved to get advantage from the technology. The framework for governance is created to be a state for making improvement. On the basis of significant appraisal, the authors offer indications for additional research into the considerable advantages of blockchain applications in the digitization of government, and the participation of blockchain structure and its utilizations to fulfill community needs of public value.
Blockchain in Healthcare

Blockchain technology supports a distributed and hybrid milieu with no requirement in the principal authority. The cryptographic principle makes the transaction secure and trustworthy in nature. In recent years, blockchain technology has come to be very popular in various contexts, especially due to the attractiveness of cryptocurrencies. In the healthcare sector, blockchain technology has emerged as a tremendous potential to develop an approach which is more patient-centric and can connect different systems to trace the disease quickly by getting more precision in electronic healthcare records. This paper discusses an advanced research and analysis in the context of healthcare. The main objective is to get the information about existing systems and their limitations and to seek a solution in the area of blockchain technology in the healthcare sector. The analysis approach consists of collection of data and its properties with a bibliometric review. The conclusion specifies that the use of blockchain technology in the healthcare system is cumulative and is also useful for the sharing of information, to trace health across the world. Hence, blockchain is playing a vital role in developing a system in the context of healthcare which will distribute information about diseases and manage the health of patients by keeping all their records.
A Framework to Evaluate Readiness of Blockchain Technology Implementation

In recent years, Blockchain technology has been recognized as one of the most important new technologies affecting businesses and society, with a very bright future ahead. This technology has been known as one of the most critical technologies of industry 4. Given that Blockchain technology is a new concept before implementing it, the organization must understand it and be prepared to accept it; otherwise, it will fail and cause irreparable damage to the organization. The main purpose of this study is to provide a framework by which organizations can evaluate their readiness to implement Blockchain technology. For this purpose, after a library study and systematic literature review, this study provided the initial research framework using the meta-synthesis method. Finally, this paper used the Lawshe model to validate the proposed framework for the organization's readiness to implement Blockchain technology. Based on the findings, the final framework includes seven main dimensions, including Blockchain-based business strategy, Operations of a distributed system, culture, People in Blockchain decentralized network, decentralized governance, technology, and Blockchain-based product traceability. Besides, 37 main criteria affected these dimensions, and it can help companies increase the level of Blockchain technology readiness. Finally, this framework is used to measure the readiness of an airline company. According to the analysis results of the analysis, Blockchain-based business strategy, culture, and Blockchain-based product traceability have a medium level of readiness. Moreover, none of the main dimensions of Blockchain technology readiness have sufficient readiness in this case study. This paper suggests practical recommendations to improve the level of Blockchain technology readiness for the managers
More than human trading zones in design research and pedagogy

How might rich, critical and nuanced concepts from Science and Technology Studies (STS) be integrated into art and design practice? Conversely, how might methodologies from art and design practice be introduced into STS? This chapter argues that trading zones between STS and design allow for the development of hybrid vocabularies that can support the emergence of new disciplines and fields of study that are better suited to understanding complex socio-technical systems in more visual and tangible ways. Specifically, we argue that by working with STS theories in multiple formats and media – from narratives, stories and films to objects, prototypes, visualizations and images – it is possible to create hybrid entities that trouble traditional binary categories. In particular, in this chapter, we are particularly interested in trading zones around theories of the posthuman and the more than human.
Manipulating SGD with Data Ordering Attacks

Machine learning is vulnerable to a wide variety of attacks. It is now well understood that by changing the underlying data distribution, an adversary can poison the model trained with it or introduce backdoors. In this paper we present a novel class of training-time attacks that require no changes to the underlying dataset or model architecture, but instead only change the order in which data are supplied to the model. In particular, we find that the attacker can either prevent the model from learning, or poison it to learn behaviours specified by the attacker. Furthermore, we find that even a single adversarially-ordered epoch can be enough to slow down model learning, or even to reset all of the learning progress. Indeed, the attacks presented here are not specific to the model or dataset, but rather target the stochastic nature of modern learning procedures. We extensively evaluate our attacks on computer vision and natural language benchmarks to find that the adversary can disrupt model training and even introduce backdoors.
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

Transformers with linearised attention (''linear Transformers'') have demonstrated the practical scalability and effectiveness of outer product-based Fast Weight Programmers (FWPs) from the '90s. However, the original FWP formulation is more general than the one of linear Transformers: a slow neural network (NN) continually reprograms the weights of a fast NN with arbitrary architecture. In existing linear Transformers, both NNs are feedforward and consist of a single layer. Here we explore new variations by adding recurrence to the slow and fast nets. We evaluate our novel recurrent FWPs (RFWPs) on two synthetic algorithmic tasks (code execution and sequential ListOps), Wikitext-103 language models, and on the Atari 2600 2D game environment. Our models exhibit properties of Transformers and RNNs. In the reinforcement learning setting, we report large improvements over LSTM in several Atari games. Our code is public.