Reviewerly/exHarmony huggingface dataset:
Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure

Abstract Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space. To clarify the mechanism of such denoising, we numerically analyzed how the activity pattern of trained networks changes in the latent space during inference. We considered the time development of the activity pattern for specific data as one trajectory in the latent space and investigated the collective behavior of these inference trajectories for many data. Our study revealed that when a cluster structure exists in the dataset, the trajectory rapidly approaches the center of the cluster. This behavior was qualitatively consistent with the concept retrieval reported in associative memory models. Additionally, the larger the noise contained in the data, the closer the trajectory was to a more global cluster. It was demonstrated that by increasing the number of the latent variables, the trend of the approach a cluster center can be enhanced, and the generalization ability of the VAE can be improved.
Wave-Particle Duality Controlled by Single-Photon Self-Entanglement

We experimentally observe that quantum duality of a single photon is controlled by its self-entanglement through a three-way quantum coherence identity V 2 + D 2 +C 2 = 1. Here V, D, C represent waveness, particleness, and self-entanglement respectively.
H-LSTM Framework for Temporal Information Retrieval in Code-Mixed Social Media Text

The contents of social media consists of mixed script information and from these type of sources one can extract temporal information. The temporal contents are difficult to be processed by any machine. The challenge here is to retrieve temporal domain information used by the users for expressing their interests on any issue. The work outlines the comparative study of different approaches of temporal data retrieval in transliterated domain. A novel handcrafted rule based technique is applied which accepts input in mixed script format and on the basis of illustrated rules, the system is suppose to extract temporal expressions from the sentence. The evaluation measures use rule-based approach validations along with the evaluations based on statistical measures. To further validate the results a voting technique is applied that selects the most valid and suitable temporal tag for that word. The experimental results suggests that the voting method performs applied here gives the best result in context to accurate temporal tagging.
Seamless Human–Robot Interaction

This chapter focuses on human–robot interaction technologies that could seamlessly support operators on their activities in a production line. The key technology that has been mobilized is Augmented Reality (AR), due to its intuitiveness and the easiness that can overlay digital information on real assets, giving more mature support to the operators. Also, another benefit of AR technology, is that operators can receive this information using wearable devices when needed, avoiding losing time waiting or moving around the assembly station where a fixed control PC exists. Following a literature review, it is described the initialization phase of an AR application, through which the system can understand the personal characteristics of the operator (such as his/her height), to give him/her a realistic feeling when the digital components are overlaid on the real ones. Additionally, during this phase, the system receives information about the station where the operator is located, to provide the respective information. Later, support functionalities for both programming and execution phases of the production are described, as well as a brief description of the software platform to which all the tools have been integrated. Lastly, a couple of use cases stemming from the automotive industry prove the described concept.
Classification of Actors in Social Networks Using RLVECO

Several activities, comprising animate and inanimate entities, can be examined by means of Social Network Analysis (SNA). Classification tasks within social network structures remain crucial research problems in SNA. Inherent and latent facts about social graphs can be effectively exploited for training Artificial Intelligence (AI) models in a bid to categorize actors/nodes as well as identify clusters with respect to a given social network. Thus, important factors such as the individual attributes of spatial social actors and the underlying patterns of relationship binding these social actors must be taken into consideration. These factors are relevant to understanding the nature and dynamics of a given social graph. In this paper, we have proposed a hybrid model: Representation Learning via Knowledge-Graph Embeddings and Convolution Operations (RLVECO) which has been modelled for studying and extracting meaningful facts from social network structures to aid in node classification and community detection problems. RLVECO utilizes an edge sampling approach for exploiting features of a social graph, via learning the context of each actor with respect to its neighboring actors, with the aim of generating vector-space embeddings per actor which are further exploited for unexpressed representations via a sequence of convolution operations. Successively, these relatively low-dimensional representations are fed as input features to a downstream classifier for solving community detection and node classification problems about a given social network.
New Educational Materials for Attracting High School Students to Engineering

The purpose of this paper is to call for cooperation in a project started by the Kyushu Society for Engineering Education to recruit active students to engineering fields. The project aims to build a collection of educational materials that helps motivate students to study in high school by understanding how basic mathematics and sciences are important in engineering and technologies. The concept and examples of the materials are presented.
Are Modern Deep Learning Models for Sentiment Analysis Brittleƒ An Examination on Part-of-Speech

Deep Neural Networks (DNNs) have achieved remarkable results in multiple Natural Language Processing (NLP) applications. However, current studies have found that DNNs can be fooled when using modified samples, namely adversarial examples. This work, specifically, examines DNNs for sentiment analysis using adversarial examples. We particularly aim to examine the impact of modifying the Part-Of-Speech (POS) of words on the input sentences. We conduct extensive experiments on different neural network models across several real-world datasets. The results demonstrate that current DNN models for sentiment analysis are brittle with perturbed noisy words that humans do not have trouble understanding. An interesting finding is that adjective words (Adj) and the combination of adjective and adverb words (Adj-Adv) provide obvious contribution to fooling sentiment analysis DNN models <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Versatile Alligator Nanostructures for Quantum Networks with Solid-State Emitters

We report design and fabrication of novel “alligator” nanostructures as both a high-Q photonic crystal cavity that helps preserve NV centers’ optical coherence and a polarization-selective mirror for high-fidelity quantum state transfer.
A Replication Study to Explore Network-Based Co-residency of Virtual Machines in the Cloud

By deploying virtual machines (VMs) on shared infrastructure in the cloud, users gain flexibility, increase scalability, and decrease their operational costs compared to on-premise infrastructure. However, a cloud environment introduces new vulnerabilities, particularly from untrusted users sharing the same physical hardware. In 2009, Ristenpart et al. demonstrated that an attacker could place a VM on the same physical hardware and extract confidential information from a target using a side-channel attack. We replicated this seminal work on cloud cartography and network-based co-residency tests on Amazon Web Services (AWS) and OpenStack cloud infrastructures. Although the Elastic Compute Cloud (EC2) cloud cartography remains similar to prior work, current mitigations deter the network-based co-residency tests. OpenStack’s cloud cartography differs from EC2’s, and we found that OpenStack was vulnerable to one network-based co-residency test. Our results indicate that co-residency threats remain a concern more than a decade after their initial description.
Digitala resurser för elevers lärande : Lärares didaktiska arbete med digitala resurser

This research has as its primary goal find out through the teachers’ point of view how subject and digital competence can be achieved in social science classes in the Swedish upper secondary school ...
Cooperative Guidance for Waypoint Following of Distributed Multi-UAV System

This paper proposes an algorithm for cooperative guidance of an unmanned aerial vehicle (UAV) formation following a set of waypoints. Waypoints are linked by straight lines to form a sequence, which effectively tasks the formation to cooperatively follow a rectilinear path between these points. Unlike in earlier known papers, we propose a Fermat’s spiral rather than a circular arc as a fillet to smooth the trajectory. This is because a Fermat’s spiral has zero curvature at its origin, which helps avoid breaks in curvature observable when using straight lines and circular arcs. To make a UAV formation and to follow the path, we propose a novel guidance vector field that is direction- and magnitude-nonuniform. This is where the novelty of this paper lies, as it proposes the vector field method for following a Fermat’s spiral. Known studies used line-of-sight (LOS) guidance to that end. In that case, the UAV formation shapes itself using consensus-based coordination topology, i.e. the group has no leader. To test the algorithm for cooperative waypoint following when smoothing is provided by fillets, we utilized complete nonlinear models of two fixed-wing UAVs, each equipped with a tuned autopilot. Numerical simulation shows the proposed approach efficient.
TrollThrottle —Raising the Cost of Astroturfing

Astroturfing, i.e., the fabrication of public discourse by private or state-controlled sponsors via the creation of fake online accounts, has become incredibly widespread in recent years. It gives a disproportionally strong voice to wealthy and technology-savvy actors, permits targeted attacks on public forums and could in the long run harm the trust users have in the internet as a communication platform. Countering these efforts without deanonymising the participants has not yet proven effective; however, we can raise the cost of astroturfing. Following the principle ‘one person, one voice’, we introduce TrollThrottle, a protocol that limits the number of comments a single person can post on participating websites. Using direct anonymous attestation and a public ledger, the user is free to choose any nickname, but the number of comments is aggregated over all posts on all websites, no matter which nickname was used. We demonstrate the deployability of TrollThrottle by retrofitting it to the popular news aggregator website Reddit and by evaluating the cost of deployment for the scenario of a national newspaper (168k comments per day), an international newspaper (268k c/d) and Reddit itself (4.9M c/d).
Tailoring Random Forest for Requirements Classification

Automated and semi-automated classifications of requirements (type and topics) are important for making requirements management more efficient. We report how we tailored a random forest approach in the EU funded project OpenReq, aiming for sufficient quality for practical use in bid projects. Evaluation with thirty thousand requirements in English from nine tender documents for rail automation systems in various countries show that user expectations are hard to meet.
Self-Supervised Learning of Depth and Pose Using Cycle Generative Adversarial Network

In recent years, large amount of ground truth data is typically required to feed into the supervised depth estimation models to produce satisfactory performance, while it is usually costly and impracticable to acquire depth ground truth. In this paper, we propose a self-supervised joint deep learning pipeline for depth and pose estimation of monocular video sequences, which uses the cycle generation adversarial network structure to extend the existing reconstruction loss function based on photometric consistency. The generation function of the algorithm learns to synthesize the adjacent image to predict the depth map and the relative target pose, and the discriminant function learns the dispersion of the monocular images to correctly classify the realism of the composite image. At the same time, a reconstruction loss function based on pose consistency is used to assist the generator function in training. Extensive experimental results on the KITTI dataset show superior performance of the proposed method.
Improving Feature’s Capability of Carrying Category-specific Information for Adversarial Domain Adaptation

Recent research has shown that generative adversarial networks (GANs) have been successfully applied in aligning features for domain adaptation. However, the extracted features might lose category-specific information, because they are distinguished as either a source or a target during the adversarial training. To address this issue, a two-stage training framework consisting of two sets of GANs and a dedicated classifier is proposed in this study. In the pretraining stage, we use an encoder-decoder-classifier structure to obtain discriminative and representative features of the source domain and use it as a reference in the subsequent training. In the adversarial training stage, two sets of GANs are used to align target-domain features with those of the source domain and transfer samples of the target domain to the source domain simultaneously. A dedicated classifier is trained along with the adversarial loss to force the generated features of the target domain to carry category-specific information, which significantly improves classification performance. The features of the source domain stay intact in the adversarial training stage. Thus, our approach can alleviate the training burden of GANs. The proposed method has been validated on digital datasets and office31 datasets. Experimental results demonstrate that an average accuracy of 96.5% and 95.1% is achieved, which leads to superior or comparable performance to state-of-the-art results.
A Partitioned Recoding Scheme for Privacy Preserving Data Publishing

There is growing interest in Differential Privacy as a disclosure limitation mechanism for statistical data. The increased attention has brought to light a number of subtleties in the definition and mechanisms. We explore an interesting dichotomy in parallel composition, where a subtle difference in the definition of a “neighboring database” leads to significantly different results. We show that by “pre-partitioning” the data randomly into disjoint subsets, then applying well-known anony-mization schemes to those pieces, we can eliminate this dichotomy. This provides potential operational benefits, with some interesting implications that give further insight into existing privacy schemes. We explore the theoretical limits of the privacy impacts of pre-partitioning, in the process illuminating some subtle distinctions in privacy definitions. We also discuss the resulting utility, including empirical evaluation of the impact on released privatized statistics.
Does the Lean Inception Methodology Contribute to the Software Project Initiation Phase?

This work aims to make a comparative analysis of the Lean Inception methodology and the Scrum methodology applied in the initiation phase of the embedded software project that consists of a small greenhouse for the indoor cultivation of sage, controlled by a cell phone application. The Lean Inception methodology is a combination of Lean Startup and Design Thinking that, at the end of the process, quickly obtains the Minimum Viable Product (MVP). Some of the questions explored in this paper were how much the Lean Inception methodology interferes in the agility of the production of MVP, how much it influences in the quality of the final product and depicting advantages in using this methodology in the project initiation phase.
Il romanés e l’alfabetizzazione dell’alunno zingaro alla scuola dei <i>gagé</i> Problematiche linguistiche e culturali

The purpose of this essay is to examine the complex linguistic and cultural problems that the Gypsy pupil inevitably encounters in the gagé school. This school is based on a system of values and behaviours that are very different from those of Roma and Sinti pupils, whose world has its own rules and identity structures that are antithetical to those of settled communities. The article analyses the origin and the constitutive features of Romanés, which is the mother tongue spoken by the Gypsies, a language still exclusively oral and therefore possessing completely different features to those of languages and cultures based on writing. Taking into account the linguistic, psycholinguistic and glottodidactic difficulties, the essay focuses on the profound performativity of Romanés and the consequences that the oral dimension has on the literacy of the Roma/Sinto student in Italian L2.
Quantization of Weights of Trained Neural Network by Correlation Maximization Method

In this paper, we present a method for quantization of neural network weights by means of maximizing correlations between the initial and quantized weights taking into account the weight density distribution in each layer. We perform the quantization after the neural network training without a subsequent post-training and compare our algorithm with linear and exponential quantization discussed in paper [8]. We tested the algorithm using the ImageNet dataset for the neural networks VGG-16, ResNet-50, Xception [2]. In the case of the ResNet-50 and Xception neural networks, the top-1 accuracy drop was 6-8% when we reserved 5-bit for storing each weight of one layer; for 6-bit, it was 1.5–3% and less than 1.5% when we reserved 7-bit. When we used 5-bit per weight of one layer, for the VGG-16 neural network the top-1 accuracy drop was around 1.5% and it was less 1% when the number of bits per weight was larger.
Gray System-Based Identification and Pre-Culling of Outliers Applied to Magnetic Sensor in Aeromagnetic Compensation

In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method.
Research on Prevention and Control of Public Hospital Security Based on System Theory

Public hospitals are a barometer of social stability and are of great significance to social security. At present, the increasing maturity of new technologies such as big data, cloud computing and mobile Internet have provided new opportunities for the innovation and upgrading of the public security prevention and control system in hospital. Based on the system theory, this paper establishes a “1+4+5” PHs security prevention and control system. First, taking an information command center as the leader. Secondly, the campus border and access control network, key area control network, campus network space control network and video patrol control network are used as skeletons. Finally, five mechanisms including information sharing and early warning mechanism, linkage and coordination mechanism, emergency command mechanism, operation and maintenance support mechanism, and assessment and evaluation mechanism are used as guarantees. In order to comprehensively improve the information and intelligence level of the public security prevention and control system in public hospitals.
Transfer learning in hierarchical dialogue topic classification with neural networks

Knowledge transfer between tasks can significantly improve the efficiency of machine learning algorithms. In supervised natural language understanding problems, this sort of improvement is critical since the availability of labelled data is usually scarce. In this paper we address the question of transfer learning between related topic classification tasks. A characteristic of our problem is that the tasks have a hierarchical relationship. Therefore, we introduce and validate how to implement the transfer exploiting this hierarchical structure. Our results for a real-world topic classification task show that the transfer can produce improvements in the behavior of the classifiers for some particular problems.
Unsupervised Visual Relationship Inference

Visual relationship inference is an essential research area for image understanding. Owing to the recent advancement of deep learning, significant signs of progress have been made in this challenging area. Standard approaches attempt to recognize visual relationships based on supervised learning by employing a carefully annotated dataset, in which images, triplets (subject-predicate-object), and bounding boxes are attached. However, preparing a large-scale dataset is very time consuming. This study proposes a novel method to infer visual relationships without image-triplet pairs. Our method tries to keep cycle consistency and plausibility of the inferred triplets. Our experimental results demonstrate that this method can infer predicates between objects in unpaired settings, and also achieving promising results using triplets parsed from external image descriptions.
A Coin-Free Oracle-Based Augmented Black Box Framework (Full Paper)

After the work of Impagliazzo and Rudich (STOC, 1989), the black box framework has become one of the main research domain of cryptography. However black box techniques say nothing about non-black box techniques such as making use of zero-knowledge proofs. Brakerski et al. introduced a new black box framework named augmented black box framework, in which they gave a zero-knowledge proof oracle in addition to a base primitive oracle (TCC, 2011). They showed a construction of a non-interactive zero knowledge proof system based on a witness indistinguishable proof system oracle. They presented augmented black box construction of chosen ciphertext secure public key encryption scheme based on chosen plaintext secure public key encryption scheme and augmented black box separation between one-way function and key agreement. In this paper we simplify the work of Brakerski et al. by introducing a proof system oracle without witness indistinguishability, named coin-free proof system oracle, that aims to give the same construction and separation results of previous work. As a result, the augmented black box framework becomes easier to handle. Since our oracle is not witness indistinguishable, our result encompasses the result of previous work.