Reviewerly/exHarmony huggingface dataset:
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level

Larger language models have higher accuracy on average, but are they better on every single instance (datapoint)? Some work suggests larger models have higher out-of-distribution robustness, while other work suggests they have lower accuracy on rare subgroups. To understand these differences, we investigate these models at the level of individual instances. However, one major challenge is that individual predictions are highly sensitive to noise in the randomness in training. We develop statistically rigorous methods to address this, and after accounting for pretraining and finetuning noise, we find that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances across MNLI, SST-2, and QQP, compared to the overall accuracy improvement of 2-10%. We also find that finetuning noise increases with model size and that instance-level accuracy has momentum: improvement from BERT-Mini to BERT-Medium correlates with improvement from BERT-Medium to BERT-Large. Our findings suggest that instance-level predictions provide a rich source of information; we therefore, recommend that researchers supplement model weights with model predictions.
HMM Based Language Identification from Speech Utterances of Popular Indic Languages Using Spectral and Prosodic Features

Language identification system (LID) is a system which automatically recognises the languages of short-term duration of unknown utterance of human beings. It recognises the discriminate features and reveals the language of utterance that belongs to. In this paper, we consider concatenated feature vectors of Mel Frequency Cepstral Coefficients (MFCC) and Pitch for designing LID. We design a reference model one for each language using 14-dimensional feature vectors using Hidden Markov model (HMM) then evaluate against all reference models of listed languages. The likelihood value of test sample feature vectors given in the evaluation is considered to decide the language of unknown utterance of test speech sample. In this paper we consider seven Indian languages for the experimental set up and the performance of system is evaluated. The average performance of the system is 89.31% and 90.63% for three states and four states HMM for 3sec test speech utterances respectively and also it is also observed that the system gives significant results with 3sec test speech for four state HMM even though we follow simple procedure.
Neural Machine Translation: Assamese–Bengali

Laskar, Sahinur Rahman Pakray, Partha Bandyopadhyay, SivajiNeural machine translation (NMT) is a state-of-the-art technique in the task of machine translation (MT), where a source-language text is converted into a target language text while preserving its meaning. NMT attracts attention because it handles sequence to sequence learning problems for variable-length source and target sentences. With the attention mechanism, the NMT system performs well in the context-analyzing ability. But it needs sufficient parallel training corpus, which is a challenge in low resource language scenario. To overcome the bar of a handy parallel corpus, there is an increase in demand for direct translation among similar language pairs. This paper investigates the NMT system for direct translation of low resource similar language pair: Assamese–Bengali. The main contribution of this work is Assamese–Bengali parallel corpus. The NMT system has achieved a bilingual evaluation understudy (BLEU) score of 7.20 for Assamese to Bengali translation and BLEU score 10.10 for Bengali to Assamese translation, respectively.
Development and Evaluation of a Framework for the Performance-Based Testing of ICT Skills

This paper addresses the development of performance-based assessment items for ICT skills, skills in dealing with information and communication technologies, a construct which is rather broadly and only operationally defined. Item development followed a construct-driven approach to ensure that test scores could be interpreted as intended. Specifically, ICT-specific knowledge as well as problem-solving and the comprehension of text and graphics were defined as components of ICT skills and cognitive ICT tasks (i.e., accessing, managing, integrating, evaluating, creating). In order to capture the construct in a valid way, design principles for constructing the simulation environment and response format were formulated. To empirically evaluate the very heterogeneous items and detect malfunctioning items, item difficulties were analyzed and behavior-related indicators with item-specific thresholds were developed and applied. The 69 item’s difficulty scores from the Rasch model fell within a comparable range for each cognitive task. Process indicators addressing time use and test-taker interactions were used to analyze whether most test-takers executed the intended processes, exhibited disengagement, or got lost among the items. Most items were capable of eliciting the intended behavior; for the few exceptions, conclusions for item revisions were drawn. The results affirm the utility of the proposed framework for developing and implementing performance-based items to assess ICT skills.
Finding the Signal in the Noise

Abstract The constituent documents of the International Criminal Court (ICC) were formulated in the 1990s, at a time when the internet was still relatively new to most of society. In the four years between the signing of the Rome Statute in 1998 and its entry into force in 2002, there was a surge in technological development. The replacement of dial-up connections by home broadband and wireless internet, the commercial launch of 3G, and the introduction of camera phones all took place during that period, followed only a few years later by the founding of Google, YouTube and Facebook. This trend has only continued. Advanced technologies have changed the way in which societies create information and share it, generating an ever-growing volume of data. The availability and accessibility to new sources of information open up opportunities for international criminal investigators, but the task of using them effectively is not without its challenges. Recent cases before the ICC indicate that digital evidence will play an increasingly central role in investigations and prosecutions. In national jurisdictions, digital evidence is now introduced in the majority of criminal cases, and there have been significant reforms in domestic statutes on evidentiary and procedural rules from admissibility to e-discovery. However, such reforms have not yet materialized at the ICC. This article assesses whether the Rome Statute and Rules of Procedure and Evidence, and their current interpretation, remain effective and appropriate in the face of technological change. The authors examine the emerging challenges related to the use and handling of digital evidence, as well as the unique nature of the internet and other dissemination channels. By drawing attention to the main issues with the existing rules and practices, and raising unresolved questions, the authors highlight the importance of reassessing rules based on obsolete assumptions and expectations as we move towards the future.
Conditions under which “Neither-Good-Nor-Bad” Information Works Effectively on User’s Trust in Agents

The effect of the provision of “Neither-Good-Nor-Bad” (NGNB) information on the trustworthiness of agents has been reported. However, in this previous study, the size of the profits generated by the NGNB information was fixed at 50% of the maximum profit, even though this ratio can vary in practical situations. Furthermore, in this experiment, all the consequences were disclosed to the users, including those that they had not chosen. In practical situations, we are often unaware of consequences that we have not chosen. In this study, we focus on these two factors and investigate the effects of them on the trustworthiness of the agents. Experimental results reveal that the agent that reliably provides the NGNB information tends to gain a greater degree of users’ trust in a situation where it is harder to obtain large profits, and when consequences that they had not chosen were not disclosed to them.
COOPNet: Multi-Modal Cooperative Gender Prediction in Social Media User Profiling

The principal way of performing user profiling is to investigate accumulated social media data. However, the problem of information asymmetry generally exists in user generated contents since users post multi-modal contents in social media freely. In this paper, we propose a novel text-image cooperation framework (COOPNet), a bridge connection network architecture that exchanges information between texts and images. First, we map the representations of both visual and sentiment enriched textual modalities into a cooperative semantic space to derive a cooperative representation. Next, the representations of texts and images are combined with their cooperative representation to exchange knowledge in the learning process. Finally, a multi-modal regression is leveraged to make cooperative decisions. Extensive experiments on the public PAN-2018 dataset demonstrate the efficacy of our framework over the state-of-the-art methods on the premise of automatic feature learning.
ADDietCoach

The aging population worldwide is expected to increase the prevalence of Alzheimer’s disease. As there is no medical curative treatment for this disease to date, alternative treatments have been applied to improve the patient’s brain and general health. One of these efforts includes providing Alzheimer’s patients with proper food and nutrition. In this paper, we propose a knowledge-powered personalized virtual coach to provide diet and nutrition assistance to patients of Alzheimer’s and/or their informal caregivers. The virtual coach is built on top of an ontology-enhanced knowledge base containing knowledge about patients, Alzheimer’s disease, food, and nutrition. Semantics-based searching and reasoning are performed on the knowledgebase to get personalized context-aware recommendation and education about healthy eating for Alzheimer’s patients. The proposed system has been implemented as a mobile application. Evaluation based on use cases has demonstrated the usefulness of this tool.
Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation

Background Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis. Objective This study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge. Methods As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis. Results We evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model’s interpretability advantage. Conclusions In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks

Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker preferences from user descriptions or their conversation histories, which are scarce for newcomers and inactive users. In this paper, we propose a few-shot personalized conversation task with an auxiliary social network. The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network. Existing methods are mainly designed to incorporate descriptions or conversation histories. Those methods can hardly model speakers with so few conversations or connections between speakers. To better cater for newcomers with few resources, we propose a personalized conversation model (PCM) that learns to adapt to new speakers as well as enabling new speakers to learn from resource-rich speakers. Particularly, based on a meta-learning based PCM, we propose a task aggregator (TA) to collect other speakers' information from the social network. The TA provides prior knowledge of the new speaker in its meta-learning. Experimental results show our methods outperform all baselines in appropriateness, diversity, and consistency with speakers.
Designing a chatbot for young diabetics: Unprecedented experiment at the endocrinology service of the casablanca university hospital

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
On Head Movements in Repeated 360° Video Quality Assessment for Standing and Seated Viewing on Head Mounted Displays

Watching 360&#x00B0; videos on head mounted displays (HMDs) allows viewers to explore scenes in all directions. In this paper, we focus on investigating the head movements of two participants for standing and seated viewing of a total of 720 360&#x00B0; videos on HMDs. The head movements were recorded in a 360&#x00B0; video quality assessment experiment which was repeated after a long and short break between sessions to study changes in viewing behavior over time. The analysis of the head movement data is provided as histograms of head rotations, head speed, head turns, and head trajectories. It is shown that the participants have their own distinct exploration behavior for standing viewing which becomes less different for seated viewing.
Towards a General Framework for ML-based Self-tuning Databases

Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimization (BO) and reinforcement learning (RL). In this work, we describe our experience when applying these methods to a database not yet studied in this context: FoundationDB. Firstly, we describe the challenges we faced, such as unknown valid ranges of configuration parameters and combinations of parameter values that result in invalid runs, and how we mitigated them. While these issues are typically overlooked, we argue that they are a crucial barrier to the adoption of ML self-tuning techniques in databases, and thus deserve more attention from the research community. Secondly, we present experimental results obtained when tuning FoundationDB using ML methods. Unlike prior work in this domain, we also compare with the simplest of baselines: random search. Our results show that, while BO and RL methods can improve the throughput of FoundationDB by up to 38%, random search is a highly competitive baseline, finding a configuration that is only 4% worse than the, vastly more complex, ML methods. We conclude that future work in this area may want to focus more on randomized, model-free optimization algorithms.
Personal Space Evaluation and Protection in Social VR

Social VR has been widely promoted and popularized in recent years. Due to the immersion characteristic of VR, although people do not have physical contact in the virtual space, they still have judgements on distance and may feel annoyed when their personal space is intruded. Social VR users want to communicate with their virtual friends in the space. Meanwhile, keep a comfortable distance from other avatars and protect their personal space. In this paper, we evaluate user's perception and comfortable level of personal distance and compare four different methods to protect personal space in social VR.
Interoperability Architecture of the ADLIFE Patient Empowerment Platform

Chronic diseases introduce challenges for the patients to manage the changing requirements of the disease. Patient empowerment activities are a critical component to assist patients in their long-term care journey. In order to be effective, patient empowerment tools need to be well-integrated with the chronic disease management tools used at the clinical sites. This paper explores and analyzes the exploitation of HL7 FHIR to design and implement an interoperable patient empowerment platform that can be seamlessly integrated with external chronic disease management and Electronic Health Record (EHR) systems
Passive Target Motion Analysis by Fusion of Linear Arrays and Sonobuoys in a Cluttered Environment

This article is devoted to the analysis of the motion of a target using information from two kinds of cooperative maritime sensors: A wireless network of sonobuoys detecting a signal emitted by a source in motion with constant velocity; Vertical antennas measuring the cosine of the elevation angles of the received signal. We prove that the trajectory of the source is observable, under nonrestrictive assumptions concerning the scenario. After thresholding, the data are surrounded by false alarms; therefore, a probabilistic data association model is employed. The joint exploitation of the measurements of the time differences of arrival together with the measurements of the cosines of the elevation angles allow estimating the trajectory of the source. The empirical performance of the maximum likelihood estimator (MLE), evaluated by extensive simulations, reaches the asymptotic performance given by the Cramér–Rao lower bound. Finally, we extend this study to the case where the energy of the data is available, after thresholding. Again, the MLE is efficient and its performance is significantly improved.
RL-IoT: Reinforcement Learning to Interact with IoT Devices

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems.
Formal Inferring the Law of Conservation of Energy from Assuming A-Priori-ness of Knowledge in a Formal Axiomatic Epistemology System Sigma

The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defined assumption of a-priori-ness of knowledge.For realizing this aim, the following work has been done: 1) a two-valued algebraic system of formal axiology has been defined precisely and applied to proper-philosophy of physics, namely, to an almost unknown (not-recognized) formal-axiological aspect of the physical law of conservation of energy; 2) the formal axiomatic epistemology-andaxiology theory Sigma has been defined precisely and applied to properphysics for realizing the above-indicated purpose.Thus, a discrete mathematical model of relationship between philosophy of physics and universal epistemology united with formal axiology has been constructed.Results: 1) By accurate computing relevant compositions of evaluation-functions within the discrete mathematical model, it is demonstrated that a formal-axiological analog of the great conservation law of proper physics is a formal-axiological law of two-valued algebra of metaphysics.(A precise algorithmic definition of the unhabitual (not-well-known) notion "formal-axiological law of algebra of metaphysics" is given.)2) The hitherto never published significantly new nontrivial scientific result of investigation presented in this article is a formal logical inference of the law of conservation of energy within the formal axiomatic theory Sigma from conjunction of the formal-axiological analog of the law of conservation of energy and the assumption of a-priori-ness of knowledge.
Surgical Smoke Dehazing and Color Reconstruction

Smoke in minimally invasive robotic surgeries appears during the cauterization of vessels and dissection of tissues. The smoke obstructs the field of view of the surgeon in real-time surgeries. Use of energy instruments for smoke removal have their own limitations. Many dehazing or desmoking algorithms reported induce color distortions and artifacts, which can mislead the surgeons. In this paper, a workflow for smoke dehazing and color restoration is proposed. We automate the process of air-light estimation using smoke features which is the key parameter for dehazing. To reduce color artifacts a color normalization is applied to the dehazed image using the detected non-smoky reference image. The results of the proposed approach shows a reduction in color artifacts and outperforms other methods reported in the literature.
Optimal parent Hamiltonians for time-dependent states

Given a generic time-dependent many-body quantum state, we determine the associated parent Hamiltonian. This procedure may require, in general, interactions of any sort. Enforcing the requirement of a fixed set of engineerable Hamiltonians, we find the optimal Hamiltonian once a set of realistic elementary interactions is defined. We provide three examples of this approach. We first apply the optimization protocol to the ground states of the one-dimensional Ising model and a ferromagnetic $p$-spin model but with time-dependent coefficients. We also consider a time-dependent state that interpolates between a product state and the ground state of a $p$-spin model. We determine the time-dependent optimal parent Hamiltonian for these states and analyze the capability of this Hamiltonian of generating the state evolution. Finally, we discuss the connections of our approach to shortcuts to adiabaticity.