Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Few-Shot Learning with Local and Global Consistency

Learning from a few examples is a key characteristic of human intelligence that AI researchers have been excited about modeling. With the web-scale data being mostly unlabeled, few recent works showed that few-shot learning performance can be significantly improved with access to unlabeled data, known as semi-supervised few shot learning (SS-FSL). We introduce a SS-FSL approach that we denote as Consistent Prototypical Networks (CPN), which builds on top of Prototypical Networks. We propose new loss terms to leverage unlabelled data, by enforcing notions of local and global consistency. Our work shows the effectiveness of our consistency losses in semi-supervised few shot setting. Our model outperforms the state-of-the-art in most benchmarks, showing large improvements in some cases. For example, in one mini-Imagenet 5-shot classification task, we obtain 70.1% accuracy to the 64.59% state-of-the-art. Moreover, our semi-supervised model, trained with 40% of the labels, compares well against the vanilla prototypical network trained on 100% of the labels, even outperforming it in the 1-shot mini-Imagenet case with 51.03% to 49.4% accuracy. For reproducibility, we make our code publicly available.
Sensing Danger: Exploiting Sensors to Build Covert Channels

Recent incidents have shown that sensor-equipped devices can be used by adversaries to perform malicious activities, such as spying on end-users or for industrial espionage. In this paper, we present a novel attack scenario that uses unsecured embedded sensors to build covert channels that can be used to bypass security mechanisms and transfer information between isolated processes. We present covert channels that require read- and write-access for sensor registers as well as a covert channel that transfers data by just triggering sensor readings so that malicious behavior cannot be distinguished from normal sensor usage. For each presented covert channel we discuss the trade-off between data rate and the likelihood of being detected as well as potential countermeasures. The fastest covert channel we implemented achieves a data rate of 4844 bit/s while the stealthiest but slower covert channel cannot be distinguished from normal user behavior. To highlight the significance of these security issues, we used popular platforms, such as Linux and Android, to evaluate the presented covert channels. However, we do not make any assumption regarding the device’s platform, and thus we believe that the presented exploits pose a significant security risk for any sensor-equipped device.
Sensitivity measurement for asymmetric two two-level atoms interacting with field obeys SU(1,1) Lie group via atomic inversion

A quantum optics model of the asymmetric case of the interaction between two two-level atoms and quantum field, which obeys SU(1,1) Lie group, is proposed. The atom-atom interaction and the rotating wave approximation are suggested in the Hamiltonian operator. Our aim is to obtain the time-dependent wave function of asymmetric case analytically. The analytical method is based on the eigenvalues and corresponding eigenvectors of the coefficient matrix of the interaction Hamiltonian operator. The SU(1,1) quantum system is initially in the Perelomov coherent state. Therefore, the atomic inversion is obtained and discussed for different values of model parameters such as initial atomic angles, Perelomov coherent, the Bargmann index and the detuning parameters. Observe that the quantum optics model is sensitive to the variation in both the Perelomov coherent parameter and the Bargmann index. In addition, there are nonclassical properties of the proposed quantum model in the presence the detuning parameter changes.
Addressing the Challenges of Situationally-Induced Impairments and Disabilities in Mobile Interaction

Situationally-induced impairments and disabilities (SIIDs) make it difficult for users of interactive computing systems to perform tasks due to context (e.g., listening to a phone call when in a noisy crowd) rather than a result of a congenital or acquired impairment (e.g., hearing damage). SIIDs are a great concern when considering the ubiquitousness of technology in a wide range of contexts. Considering our daily reliance on technology, and mobile technology in particular, it is increasingly important that we fully understand and model how SIIDs occur. Similarly, we must identify appropriate methods for sensing and adapting technology to reduce the effects of SIIDs. In this workshop, we will bring together researchers working on understanding, sensing, modelling, and adapting technologies to ameliorate the effects of SIIDs. This workshop will provide a venue to identify existing research gaps, new directions for future research, and opportunities for future collaboration.
An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

AbstractExisting bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern...
Improving MAE against CCE under Label Noise.

Label noise is inherent in many deep learning tasks when the training setbecomes large. A typical approach to tackle noisy labels is using robust lossfunctions. Categorical cross entropy (CCE) is a successful loss function inmany applications. However, CCE is also notorious for fitting samples withcorrupted labels easily. In contrast, mean absolute error (MAE) isnoise-tolerant theoretically, but it generally works much worse than CCE inpractice. In this work, we have three main points. First, to explain why MAEgenerally performs much worse than CCE, we introduce a new understanding ofthem fundamentally by exposing their intrinsic sample weighting schemes fromthe perspective of every sample's gradient magnitude with respect to logitvector. Consequently, we find that MAE's differentiation degree over trainingexamples is too small so that informative ones cannot contribute enough againstthe non-informative during training. Therefore, MAE generally underfitstraining data when noise rate is high. Second, based on our finding, we proposean improved MAE (IMAE), which inherits MAE's good noise-robustness. Moreover,the differentiation degree over training data points is controllable so thatIMAE addresses the underfitting problem of MAE. Third, the effectiveness ofIMAE against CCE and MAE is evaluated empirically with extensive experiments,which focus on image classification under synthetic corrupted labels and videoretrieval under real noisy labels.
Image search using multilingual texts: a cross-modal learning approach between image and text Maxime Portaz Qwant Research.

Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different languages. In this paper, we propose to embed images and texts into a unique distributional vector space, enabling to search images by using text queries expressing information needs related to the (visual) content of images, as well as using image similarity. Our framework forces the representation of an image to be similar to the representation of the text that describes it. Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images. We provide experimental evidence of the efficiency of our approach by experimenting it on two datasets: Common Objects in COntext (COCO) [19] and Multi30K [7].
Sentiment analysis of Arabic comparative opinions

The rapid development of social media platforms allowed opinion mining research to increase significantly. Opinion mining/sentiment analysis is the process of extracting subjective information from opinions that imply single sentiment. Comparative opinion mining is a sub-field of opinion mining that deals with multi-sentiment opinions. Such opinions are expressed by comparing several entities to each other. The sentiment of a comparative relation is recognized by identifying the relation’s direction and thus the preferred entity. This paper proposes an unsupervised sentiment analysis technique for Arabic comparative opinions to identify the preferred entity. The proposed technique considers three main elements when analyzing comparative opinions: The type of comparative keywords, the existence of features in the opinion, and the entities’ position to the comparative keyword. Five main categories are proposed for classifying comparative keywords, which facilitates the analysis of each comparative sentence. The proposed technique limits the need for human interference to the initial steps of preparing the lexicons, collecting and categorizing comparative keywords. Furthermore, the proposed technique handles opinions that do not contain features at all. The results are very promising with a total average of 96.5% f-measure of correctly identified sentiment.
The Influence of Personality on Code Reuse

The ubiquity and necessity of computer software requires programmers to reuse extant code to keep up with increasing software demands.Researchers have started to investigate the underlying psychological processes and the programmer characteristics affecting code reuse.The present study investigated the role of programmer personality (propensity to trust, suspicion propensity) on willingness to reuse code.Programmers were recruited through Amazon's Mechanical Turk.Programmers completed propensity to trust and suspicion personality inventories and were subsequently presented with 18 pieces of computer code containing transparency and reputation manipulations.The results demonstrated that propensity to trust did not influence willingness to reuse code.However, facets of suspicion propensity did affect reuse willingness.Programmers lower in trait mal-intent perceptions and higher in cognitive activity were more likely to report they would reuse code.Implications and applications are discussed.
An Efficient Virtual Machine Placement via Bin Packing in Cloud Data Centers

Virtual machine (VM) consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM consolidation includes a most important subproblem, i.e., VM placement problem. The basic objective of VM placement is to minimize the use of running physical machines (PMs). An enhanced levy based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving VM placement problem. Moreover, the best fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are performed to check the performance of the proposed algorithm. The proposed algorithm is compared with simple particle swarm optimization (PSO) and the hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. Matlab is used for simulations.
A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.
A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed.
Key aspects of digital image correlation in impact tests of reinforced concrete beams

&lt;p&gt;This paper studies 2-D high speed photography combined with digital image correlation (DIC) applied to experimental research of reinforced concrete beams at moderate loading rates. The aim of the present research is to understand the influence of 2-D DIC set-up parameters in the results. Drop-weight tests have been completed in 1180 × 100 × 100 mm longitudinally reinforced concrete beams. The study has confirmed results sensitivity to image subdivision and mesh properties. While smaller subdivision sizes allow to obtain results nearby boundaries, being more suitable to study local effects, larger sizes enhance computational cost, increase mesh stability and accuracy. A discussion of key aspects of 2-D DIC for measuring different parameters (such as acceleration, displacements, strains and strain-rate) is presented along this paper.&lt;/p&gt;
Appraisal of Climatic Conditions for Potential Solar Energy Applications in Nawabshah and Quetta Cities

This study was conducted to appraise climatic conditions for potential applications of solar energy units in Nawabshah and Quetta cities. For that, twenty two annual monthly average data of insolation on horizontal surface, at various slopes on titled surface, air temperature, earth skin temperature was acquired and evaluated through the Statistical Package for the Social Sciences software. It is discovered from the study that Quetta is a more promising place for installation of solar energy applications as compared to Nawabshah as it receives more solar radiations and less air temperature.
Divided-aperture subtraction-differential confocal method with nanoscale axial resolution

In this paper, a divided-aperture subtraction-differential confocal method (DASDCM) is proposed to meet the requirements of nanoscale noncontact height measurements for precision machining, materials science, and biology. The DASDCM divides the spot on the detection focal plane into two groups of circular detection areas, which are symmetrical to the optical axis and consist of two concentric detection pinholes with different sizes in each group. Then, the DASDCM uses a subtraction of the intensity signals received from the two detection pinholes in each group to suppress the interference of the nonconjugated information on the intensity signal; it also uses the differential subtraction of two obtained circular detection signals to obtain a sensitive axial response curve. Thereby, the DASDCM greatly improves the axial resolution while considering the signal-to-noise ratio and axial dynamic range of the system and can realize surface height measurement without axial scanning by using the linear range of the axial response curve. Theoretical analysis and preliminary experiments show that DASDCM has an axial resolution of 2&#x00A0;nm with a laser wavelength of &#x03BB;=632.8&#x2009;&#x2009;nm and a numerical aperture of NA=0.8. It provides an effective technique for nanoscale height detection with high axial resolution.
Leveraging Anomaly Detection in Business Process with Data Stream Mining

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nonetheless, the continuous nature of business activities conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. Exploring different combinations of parameters, we obtained promising performance metrics, showing that our method is capable of finding anomalous process instances in a vast complexity of scenarios.
Death: The Simple Clinical Trial Endpoint.

Death, as a biological phenomenon, is well understood and a commonly employed endpoint for clinical trials. However, death identification and adjudication may be difficult for pragmatic clinical trials (PCT) that rely upon electronic health record and patient reported data. We propose a novel death identification and verification approach that is being used in the ToRsemide compArisoN with furoSemide FOR Management of Heart Failure (TRANSFORM-HF) PCT. We describe our hybrid approach that includes gathering information from clinical trial sites, a centralized call center, and National Death Index searches. Our methods detail how a possible death is triggered from each of these components and the types of information we require to verify a triggered death. Our different trigger/verification elements collectively define the TRANSFORM-HF PCT's definition of a death event.
A Novel Face Detection Method Based on Over-complete Incoherent Dictionary Learning

In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the proposed method involves the learning of comprehensive models with atoms that have the highest atom/data coherence with the training data and the lowest within-class and between-class coherence parameters. Each of these goals can be achieved through the proposed procedures. In order to achieve the desired results, a variety of features are extracted from the images and used to learn the characteristics of face and non-face images. Also, the results of the proposed classifier based on the incoherent dictionary learning technique are compared with the results obtained from the other common classifiers such as neural network and support vector machine. Simulation results, along with a significance statistical test show that the proposed method based on the incoherent models learned by the combinational features is able to detect the face regions with high accuracy rate.
Natural conversation framework

With recent advances in natural language understanding techniques and farfield microphone arrays, natural language interfaces, such as voice assistants and chatbots, are emerging as a popular new way to interact with computers. They have made their way out of the industry research labs and into the pockets, desktops, cars and living rooms of the general public. But although such interfaces recognize bits of natural language, and even voice input, they generally lack conversational competence, or the ability to engage in natural conversation. Today's platforms provide sophisticated tools for analyzing language and retrieving knowledge, but they fail to provide adequate support for modeling interaction. The user experience (UX) designer or software developer must figure out how a human conversation is organized, usually relying on commonsense rather than on formal knowledge. Fortunately, practitioners can rely on conversation science. This book adapts formal knowledge from the field of Conversation Analysis (CA) to the design of natural language interfaces. It outlines the Natural Conversation Framework (NCF), developed at IBM Research, a systematic framework for designing interfaces that work like natural conversation. The NCF consists of four main components: 1) an interaction model of "expandable sequences," 2) a corresponding content format, 3) a pattern language with 100 generic UX patterns and 4) a navigation method of six basic user actions. The authors introduce UX designers to a new way of thinking about user experience design in the context of conversational interfaces, including a new vocabulary, new principles and new interaction patterns. User experience designers and graduate students in the HCI field as well as developers and conversation analysis students should find this book of interest.
Facial expressions analysis under occlusions based on specificities of facial motion propagation

Although much progress has been made in the facial expression analysis field, facial occlusions are still challenging. The main innovation brought by this contribution consists in exploiting the specificities of facial movement propagation for recognizing expressions in presence of important occlusions. The movement induced by an expression extends beyond the movement epicenter. Thus, the movement occurring in an occluded region propagates towards neighboring visible regions. In presence of occlusions, per expression, we compute the importance of each unoccluded facial region and we construct adapted facial frameworks that boost the performance of per expression binary classifier. The output of each expression-dependant binary classifier is then aggregated and fed into a fusion process that aims constructing, per occlusion, a unique model that recognizes all the facial expressions considered. The evaluations highlight the robustness of this approach in presence of significant facial occlusions.
Affinity-based measures of biomarker performance evaluation

We propose new summary measures of biomarker accuracy which can be used as companions to existing diagnostic accuracy measures. Conceptually, our summary measures are tantamount to the so-called Hellinger affinity and we show that they can be regarded as measures of agreement constructed from similar geometrical principles as Pearson correlation. We develop a covariate-specific version of our summary index, which practitioners can use to assess the discrimination performance of a biomarker, conditionally on the value of a predictor. We devise nonparametric Bayes estimators for the proposed indexes, derive theoretical properties of the corresponding priors, and assess the performance of our methods through a simulation study. The proposed methods are illustrated using data from a prostate cancer diagnosis study.