Reviewerly/exHarmony huggingface dataset:
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels

Explaining to users why automated systems make certain mistakes is important and challenging. Researchers have proposed ways to automatically produce interpretations for deep neural network models. However, it is unclear how useful these interpretations are in helping users figure out why they are getting an error. If an interpretation effectively explains to users how the underlying deep neural network model works, people who were presented with the interpretation should be better at predicting the model’s outputs than those who were not. This paper presents an investigation on whether or not showing machine-generated visual interpretations helps users understand the incorrectly predicted labels produced by image classifiers. We showed the images and the correct labels to 150 online crowd workers and asked them to select the incorrectly predicted labels with or without showing them the machine-generated visual interpretations. The results demonstrated that displaying the visual interpretations did not increase, but rather decreased, the average guessing accuracy by roughly 10%.
Indoor channel modeling for continuous variable quantum key distribution in the terahertz band

Continuous-variable quantum key distribution (CVQKD) in an indoor scenario can provide secure wireless access for practical short-distance communications with high rates. However, a suitable channel model for implementing the indoor CVQKD system has not been considered before. Here, we establish an indoor channel model to show the feasibility of CVQKD in terahertz (THz) band. We adopt both active and passive state preparation schemes to demonstrate the performance of the indoor CVQKD system involving multi-path propagation. We achieve the channel transmittance characterized by frequency, water-vapor density, antenna gain, reflection loss and the surrounding itself. The ray-tracing based numerical simulations show that the multi-path propagation can degrade the performance of the indoor CVQKD system. The maximum transmission distance is two meters at 410 GHz for both active and passive state preparations, and it can be extended to 35 and 20 meters respectively by using high gain antenna to combat the multi-path propagation.
Automated versus Manual Approach of Web Application Penetration Testing

The main aim of this work is to find and explain certain scenarios that can demonstrate the differences in automated and manual approaches for penetration testing. There are some scenarios in which manual testing works better than automatic scripts/vulnerability scanners for finding security issues in web applications. In some other scenarios, the opposite may be true. The concepts of various web application vulnerabilities have been used for testing, including OWASP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Open Web Application Security Project; online community dedicated to web security Top 10, using both manual and automatic approaches. Automation tools and scripts have been used and tested to see what could potentially go wrong if attackers exploit such vulnerabilities. Also, certain scenarios have been used which determine whether one approach is better than the other for finding/detecting security issues in web applications. Finally, the work concludes by providing results in the form of pros-and-cons of both approaches, which it realises after carrying this out.
Unsupervised Learning for Sequence-to-Sequence Text-to-Speech for Low-Resource Languages

Recently, sequence-to-sequence models with attention have been successfully applied in Text-to-speech (TTS). These models can generate near-human speech with a large accurately-transcribed speech corpus. However, preparing such a large data-set is both expensive and laborious. To alleviate the problem of heavy data demand, we propose a novel unsupervised pre-training mechanism in this paper. Specifically, we first use Vector-quantization Variational-Autoencoder (VQ-VAE) to ex-tract the unsupervised linguistic units from large-scale, publicly found, and untranscribed speech. We then pre-train the sequence-to-sequence TTS model by using the pairs. Finally, we fine-tune the model with a small amount of paired data from the target speaker. As a result, both objective and subjective evaluations show that our proposed method can synthesize more intelligible and natural speech with the same amount of paired training data. Besides, we extend our proposed method to the hypothesized low-resource languages and verify the effectiveness of the method using objective evaluation.
SODA: Story Oriented Dense Video Captioning Evaluation Framework

Dense Video Captioning (DVC) is a challenging task that localizes all events in a short video and describes them with natural language sentences. The main goal of DVC is video story description, that is, to generate a concise video story that supports human video comprehension without watching it. In recent years, DVC has attracted increasing attention in the vision and language research community, and has been employed as a task of the workshop, ActivityNet Challenge. In the current research community, the official scorer provided by ActivityNet Challenge is the de-facto standard evaluation framework for DVC systems. It computes averaged METEOR scores for matched pairs between generated and reference captions whose Intersection over Union (IoU) exceeds a specific threshold value. However, the current framework does not take into account the story of the video or the ordering of captions. It also tends to give high scores to systems that generate several hundred redundant captions, that humans cannot read. This paper proposes a new evaluation framework, Story Oriented Dense video cAptioning evaluation framework (SODA), for measuring the performance of video story description systems. SODA first tries to find temporally optimal matching between generated and reference captions to capture the story of a video. Then, it computes METEOR scores for the matching and derives F-measure scores from the METEOR scores to penalize redundant captions. To demonstrate that SODA gives low scores for inadequate captions in terms of video story description, we evaluate two state-of-the-art systems with it, varying the number of captions. The results show that SODA gives low scores against too many or too few captions and high scores against captions whose number equals to that of a reference, while the current framework gives good scores for all the cases. Furthermore, we show that SODA tends to give lower scores than the current evaluation framework in evaluating captions in the incorrect order.
Gender Identification through Handwriting: an Online Approach

The present study was designed to identify writer's gender trough online handwriting and drawing analysis. Two groups - one of 126 males (mean age 24.65, SD=2.45) and the other of 114 females (mean age 24.51, SD=2.50) participants were recruited in the experiment. They were asked to perform seven writing and drawing tasks utilizing a digitizing tablet and a special writing device. Seventeen writing features grouped into five categories have been considered. The experiment's results show that the set of considered features enable to discriminate between male and female writers investigating their performance while copying a house drawing (task 2), writing words in capital letters (task 3) and writing a complete sentence in cursive letters (task 7), in particular focusing on Ductus (number of strokes) and Time categories of writing features.
BoxE: A Box Embedding Model for Knowledge Base Completion

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding

Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.Some traditional KGE models leveraging additional type information can improve the representation of entities which however totally rely on the explicit types or neglect the diverse type representations specific to various relations.Besides, none of the existing methods is capable of inferring all the relation patterns of symmetry, inversion and composition as well as the complex properties of 1-N, N-1 and N-N relations, simultaneously.To explore the type information for any KG, we develop a novel KGE framework with Automated Entity TypE Representation (AutoETER), which learns the latent type embedding of each entity by regarding each relation as a translation operation between the types of two entities with a relationaware projection mechanism.Particularly, our designed automated type representation learning mechanism is a pluggable module which can be easily incorporated with any KGE model.Besides, our approach could model and infer all the relation patterns and complex relations.Experiments on four datasets demonstrate the superior performance of our model compared to state-of-the-art baselines on link prediction tasks, and the visualization of type clustering provides clearly the explanation of type embeddings and verifies the effectiveness of our model.
CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP

This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make use of the concept of weights in our design to analyze the transition of hidden states. At the same time, we also describe how the content adaptive gate handles the received words and alleviates the long-term dependence problem. As a result, the unit can improve the accuracy of the experiments, and the results show that CARU not only has better performance than GRU, but also produces faster training. Moreover, the proposed unit is general and can be applied to all RNN related neural network models.
Hold me tight! Influence of discriminative features on deep network boundaries

Important insights towards the explainability of neural networks reside in the characteristics of their decision boundaries. In this work, we borrow tools from the field of adversarial robustness, and propose a new perspective that relates dataset features to the distance of samples to the decision boundary. This enables us to carefully tweak the position of the training samples and measure the induced changes on the boundaries of CNNs trained on large-scale vision datasets. We use this framework to reveal some intriguing properties of CNNs. Specifically, we rigorously confirm that neural networks exhibit a high invariance to non-discriminative features, and show that the decision boundaries of a DNN can only exist as long as the classifier is trained with some features that hold them together. Finally, we show that the construction of the decision boundary is extremely sensitive to small perturbations of the training samples, and that changes in certain directions can lead to sudden invariances in the orthogonal ones. This is precisely the mechanism that adversarial training uses to achieve robustness.
Phenotypes to remember: Evolutionary developmental memory capacity and robustness

There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental “devo-engrams” are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms.
Problem-based Learning during the COVID-19 Pandemic: Can Project Groups Save the Day?

In this paper, we describe how a Danish problem-based learning university adapted to the circumstances surrounding the coronavirus disease of 2019 (COVID-19) pandemic.Our findings reveal that digital problem-based learning mitigated some negative consequences that appeared with the lockdown and resulting shift to 100 percent online teaching.While students prefer the traditional face-to-face teaching mode due to the energy, variation, and socialization associated with on-campus learning, we observed that students who have worked in project groups had a more positive experience with online learning, which indicates belonging to a project group can increase students' motivation to participate in online teaching activities.Our findings challenge the idea that problem-based learning revolves around face-to-face interaction on campus.We highlight lessons that we learned when we rapidly shifted to online distance teaching.
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization

Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks, including automatic summarization. However, most work has focused on (relatively) data-rich single-document summarization settings. In this paper, we explore highly-abstractive multi-document summarization, where the summary is explicitly conditioned on a user-given topic statement or question. We compare the summarization quality produced by three state-of-the-art transformer-based models: BART, T5, and PEGASUS. We report the performance on four challenging summarization datasets: three from the general domain and one from consumer health in both zero-shot and few-shot learning settings. While prior work has shown significant differences in performance for these models on standard summarization tasks, our results indicate that with as few as 10 labeled examples, there is no statistically significant difference in summary quality, suggesting the need for more abstractive benchmark collections when determining state-of-the-art.
Improving Extreme Learning Machine Accuracy Utilizing Genetic Algorithm for Intrusion Detection Purposes

Intrusion detection system (IDS) is a kind of software protection which is developed in order to automatically provide alarms to the supervisor when someone or something tries penetrating the system information by means of malignant activity or by means of security strategy infringements. The extreme learning machine (ELM) is an easy knowledge algorithm for hidden single-layer neural networks SLFNs whose knowledge velocity can be thousands of times quicker than the traditional feeding network learning algorithms such as reverse propagation algorithm (BP) while getting best popularization execution, but the main ELM problem is not more accurate. Genetic algorithms (GAs) have become common as a way in order to provide solutions to the hard combinatorial optimization problems. In this paper, the genetic algorithm will be utilized in order to enhance the ELM accuracy for the IDS purposes. The proposed enhancement is by working and preparing the inputs of the ELM before the processing, and then, the ELM result will be utilized to determine the intrusion.
Design and Deployment of an Image Polarity Detector with Visual Attention

Abstract Embedding the ability of sentiment analysis in smart devices is especially challenging because sentiment analysis relies on deep neural networks, in particular, convolutional neural networks. The paper presents a novel hardware-friendly detector of image polarity, enhanced with the ability of saliency detection. The approach stems from a hardware-oriented design process, which trades off prediction accuracy and computational resources. The eventual solution combines lightweight deep-learning architectures and post-training quantization. Experimental results on standard benchmarks confirmed that the design strategy can infer automatically the salient parts and the polarity of an image with high accuracy. Saliency-based solutions in the literature prove impractical due to their considerable computational costs; the paper shows that the novel design strategy can deploy and perform successfully on a variety of commercial smartphones, yielding real-time performances.
Deep auto encoders to adaptive E-learning recommender system

Adaptive learning, supported by Information & Communication Technology (TIC), is an important research area for educational systems which aim to improve the outcomes of students. Thus, the investigation of what should be adapted and how much to adapt constitute a foundation to Adaptive E-learning Systems (AES). In this paper, we compared three classes of Deep Auto Encoders and the popularity model to address the problem of learning and predicting the preferences of student on AES: Collaborative Denoising Auto Encoders (CDAE), Deep Auto Encoders for Collaborative Filtering (DAE-CF), and Deep Auto Encoders for Collaborative Filtering using Content Information (DAE-CI). The results point out that the DAE-CF is more effective providing significant adaptability. Furthermore, we present the concept named as signature of preference to represent a more granular class of adaptability. Therefore, this model may be used in e-learning systems to provide adaptability and help to improve the outcomes of students.
Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project

Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes. We propose a Volcano-Independent VSR (VI.VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI.VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN.ears ecosystem to facilitate the VI.VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio , and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI.VSR models in challenging scenarios, obtaining encouraging recognition results in the 70–80% accuracy range. VI.VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.
VISIONE at Video Browser Showdown 2021

This paper presents the second release of VISIONE, a tool for effective video search on large-scale collections. It allows users to search for videos using textual descriptions, keywords, occurrence of objects and their spatial relationships, occurrence of colors and their spatial relationships, and image similarity. One of the main features of our system is that it employs specially designed textual encodings for indexing and searching video content using the mature and scalable Apache Lucene full-text search engine.
36.2 An EM/Power SCA-Resilient AES-256 with Synthesizable Signature Attenuation Using Digital-Friendly Current Source and RO-Bleed-Based Integrated Local Feedback and Global Switched-Mode Control

Mathematically secure cryptographic algorithms leak side-channel information in the form of correlated power and electromagnetic (EM) signals, leading to physical sidechannel analysis (SCA) attacks. Circuit-level countermeasures against power/EM SCA include current equalizer [1], series LDO [2], IVR [3], enhancing protection up to 10M traces. Recently, current domain signature attenuation [4] and randomized NL-LDO cascaded with arithmetic countermeasures [5] achieved >1B minimum traces to disclosure (MTD) with a single and two countermeasures, respectively. Among these, the highest protection with a single strategy is achieved using signature attenuation [4], [6], which utilized a current source making the supply current mostly constant. While being highly resilient to SCA, [4] required analog-biased cascode current sources and an analog bleed path, making it not easily scalable across different technology generations. Conversely, [2], [5] are synthesizable but a single countermeasure only achieved moderate protection (up to 10M MTD). This work embraces the concept of signature attenuation in the current domain, but makes it fully-synthesizable with digital current sources, control loop and the bleed to increase the MTD from 10M [5] to 250M (25x improvement, Fig. 36.2.1) using a single synthesizable countermeasure. Finally, combining the digital signature attenuation circuit (DSAC) with a second synthesizable generic technique in the form of a time-varying transfer function (TVTF), this work achieves an MTD>1.25B for both EM and power SCA.
Adiabatic Spectroscopy and a Variational Quantum Adiabatic Algorithm

A toolbox for variational quantum adiabatic algorithms is proposed that allows to combine the strengths of the adiabatic and the variational approaches for fast and high-fidelity ground state preparation.