Reviewerly/exHarmony huggingface dataset:
Highly adaptive and energy efficient neuromorphic computation enabled by deep-spike heterostructure photonic neuro-transistors

Recently, neuromorphic photonics using optical signal as a data domain are considered as a promising solution to realize the next generation neural network platform. Here, metal-chalcogenide/metal oxide semiconductor based photonic neuro-transistors with deep spike-like heterostructure are proposed as a highly adaptive and energy efficient neuromorphic device. In particular, the energy band structure of cadmium sulfide (CdS)/amorphous indium-gallium-zinc-oxide (a-IGZO) heterojunction is engineered via mediating the anion-to-cation ratio of CdS films. It is revealed that the S/Cd ratio is able to determine the work function of the film which consequently causes a variation in the degree of band-bending at the heterointerface. Using a CdS film with optimized S/Cd ratio (CdS1.2), deep spike-like heterostructure (DHS) can be constructed which enables efficient accumulation of photo-generated charge carriers and the emulation of biological synaptic functions including long-term potentiation (LTP) and depression (LTD) behaviors. Also, the a-IGZO/CdS1.2 DHS transistor exhibits low non-linearity value for LTP (1.1) and less energy consumption (45.04 pJ). Furthermore, 7 × 7 opteoelectronic neuromorphic arrays are successfully implemented to exhibit possibility of realization of hardware-based weight pixel training. In addition, the a-IGZO/CdS1.2 DHS transistor shows a high accuracy for image pattern recognition (85.96%) based on the artificial neural network simulation, proving the feasibility in the artificial intelligent systems.
Three Interactive Add-ons for Small Local Playgrounds: Towards Designing for Context-sensitive Play Activities

There is an observed reduction in outdoor play in many parts of the world. Although interactive playgrounds exist that are placed at larger playgrounds, such interactive setups in the close vicinity are not yet omnipresent. In this interactivity contribution we showcase three interactive add-on prototypes for the small local playground equipment, that span a spectrum of opportunities. We include a story-based themed interaction of Dragon School, for instance breathing as a dragon with an echo effect. The second interaction Nuppu is a higher intensity variation of the add-on, where playground equipment have to be ‘conquered’ by performing the appropriate action (i.e. swinging makes the swing go through a variation of colors). The third interaction Magic Climb, is a more location-specific design for climbing equipment where animal-themed stations are lit up in random order making animal sounds, after the accompanying sound is played they be activated by an action related to that animal such as floating your hand as a bee above a sensor triggering the next sound. Especially for the latter setup, it was easy to come up with different game variations by changing the rules kept by the players but using the same system interaction. However, that idea embodies through its variation a question that becomes more and more important and we want to discuss: How to design for cultivating this type of open-ended play towards others sessions and players in a digital age? As we know open-ended play is a promising endeavor where the activity of coming up with games is part of the game. At the same time we think for children new systems should be able to be used in some form within a short amount of time, and provide enough perspective on how to use them immediately.
Voice Conversion of Tagalog Synthesized Speech Using Cycle-Generative Adversarial Networks (Cycle-GAN)

Existing Tagalog Text-to-speech (TTS) systems still have room for improvement, and although recent attempts at creating local TTS systems for Philippine spoken languages were able to generate synthesized speech, they still possess relatively low Mean Opinion Scores (MOS), ranging from 1.5 to 3.9 (out of 5), when it comes to naturalness and intelligibility. Improving speech prosody, the main factor for a speech's naturalness or individuality, has been made possible through voice conversion (VC). This project aims to implement a VC system for Tagalog synthesized speech, specifically using Cycle Generative Adversarial Networks (Cycle-GAN), a state-of-the-art neural network architecture used in non-parallel VC. Inter-gender and intra-gender VC were made for two types of inputs: Google's own Tagalog TTS and a locally sourced TTS system built from Mary TTS. Results show that Google TTS and its VC models perform better overall than Mary TTS and its VC models. Mel Cepstral Distortions (MCD) and F0: Root Mean Square Errors (F0:RMSE) vary across all models, reaching an MCD as low as 6.52 dB for Google TTS' intra-gender VC and an F0:RMSE as low as 16.92 Hz from Google TTS' inter-gender VC. Meanwhile, undergoing VC also caused a degradation in perceived speech quality as seen in a decrease in MOS across all VC models. Inter-gender VC for both TTS inputs were subjectively more preferred over intra-gender VC, reaching MOS values of 3.76 and 2.32 for Google TTS and Mary TTS inputs, respectively. Furthermore, it was also shown that male respondents were likely to rate higher opinion scores for intra-gender VC than female respondents, likely due to differences in hearing sensitivities.
MetaEmu

In this paper we present MetaEmu, an architecture-agnostic framework geared towards rehosting and security analysis of automotive firmware. MetaEmu improves over existing rehosting environments in two ways: Firstly, it solves the hitherto open-problem of a lack of generic Virtual Execution Environments (VXEs) by synthesizing processor simulators from Ghidra's language definitions. Secondly, MetaEmu can rehost and analyze multiple targets, each of different architecture, simultaneously, and share analysis facts between each target's analysis environment, a technique we call inter-device analysis.
Batching, Aggregation, and Zero-Knowledge Proofs in Bilinear Accumulators

An accumulator is a cryptographic primitive that allows a prover to succinctly commit to a set of values while being able to provide proofs of (non-)membership. A batch proof is an accumulator proof that can be used to prove (non-)membership of multiple values simultaneously.
Proof-of-Possession for KEM Certificates using Verifiable Generation

Certificate authorities in public key infrastructures typically require entities to prove possession of the secret key corresponding to the public key they want certified. While this is straightforward for digital signature schemes, the most efficient solution for public key encryption and key encapsulation mechanisms (KEMs) requires an interactive challenge-response protocol, requiring a departure from current issuance processes. In this work we investigate how to non-interactively prove possession of a KEM secret key, specifically for lattice-based KEMs, motivated by the recently proposed KEMTLS protocol which replaces signature-based authentication in TLS 1.3 with KEM-based authentication. Although there are various zero-knowledge (ZK) techniques that can be used to prove possession of a lattice key, they yield large proofs or are inefficient to generate. We propose a technique called verifiable generation, in which a proof of possession is generated at the same time as the key itself is generated. Our technique is inspired by the Picnic signature scheme and uses the multi-party-computation-in-the-head (MPCitH) paradigm; this similarity to a signature scheme allows us to bind attribute data to the proof of possession, as required by certificate issuance protocols. We show how to instantiate this approach for two lattice-based KEMs in Round 3 of the NIST post-quantum cryptography standardization project, Kyber and FrodoKEM, and achieve reasonable proof sizes and performance. Our proofs of possession are faster and an order of magnitude smaller than the previous best MPCitH technique for knowledge of a lattice key, and in size-optimized cases can be comparable to even state-of-the-art direct lattice-based ZK proofs for Kyber. Our approach relies on a new result showing the uniqueness of Kyber and FrodoKEM secret keys, even if the requirement that all secret key components are small is partially relaxed, which may be of independent interest for improving efficiency of zero-knowledge proofs for other lattice-based statements.
VRust

Solana is a rapidly-growing high-performance blockchain powered by a Proof of History (PoH) consensus mechanism and a novel stateless programming model that decouples code from data. With parallel execution on the PoH Sealevel runtime (instead of PoW), it achieves 100X-1000X speedups compared to Ethereum in terms of transactions per second. With the new programming model, new constraints (owner, signer, keys, bump seeds) and vulnerabilities (missing checks, overflows, type confusion, etc.) must be carefully verified to ensure the security of Solana smart contracts.
WINK

Sensitive numbers play an unparalleled role in identification and authentication. Recent research has revealed plenty of side-channel attacks to infer keystrokes, which require either a training phase or a dictionary to build the relationship between an observed signal disturbance and a keystroke. However, training-based methods are unpractical as the training data about the victim are hard to obtain, while dictionary-based methods cannot infer numbers, which are not combined according to linguistic rules like letters are. We observe that typing a number creates not only a number of observed disturbances in space (each corresponding to a digit), but also a sequence of periods between each disturbance. Based upon existing work that utilizes inter-keystroke timing to infer keystrokes, we build a novel technique called WINK that combines the spatial and time domain information into a spatiotemporal feature of keystroke-disturbed wireless signals. With this spatiotemporal feature, WINK can infer typed numbers without the aid of any training. Experimental results on top of software-defined radio platforms show that WINK can vastly reduce the guesses required for breaking certain 6-digit PINs from 1 million to as low as 16, and can infer over 52% of user-chosen 6-digit PINs with less than 100 attempts.
I'm SPARTACUS, No, I'm SPARTACUS

Phishing is a ubiquitous and increasingly sophisticated online threat. To evade mitigations, phishers try to "cloak" malicious content from defenders to delay their appearance on blacklists, while still presenting the phishing payload to victims. This cat-and-mouse game is variable and fast-moving, with many distinct cloaking methods---we construct a dataset identifying 2,933 real-world phishing kits that implement cloaking mechanisms. These kits use information from the host, browser, and HTTP request to classify traffic as either anti-phishing entity or potential victim and change their behavior accordingly.
VOProof

The design of zkSNARKs is increasingly complicated and requires familiarity with a broad class of cryptographic and algebraic tools. This complexity in zkSNARK design also increases the difficulty in zkSNARK implementation, analysis, and optimization. To address this complexity, we develop a new workflow for designing and implementing zkSNARKs, called VOProof. In VOProof, the designer only needs to construct a Vector Oracle (VO) protocol that is intuitive and straightforward to design, and then feeds this protocol to our VO compiler to transform it into a fully functional zkSNARK. This new workflow conceals most algebraic and cryptographic operations inside the compiler, so that the designer is no longer required to understand these cumbersome and error prone procedures. Moreover, our compiler can be fine-tuned to compile one VO protocol into multiple zkSNARKs with different tradeoffs.
Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users' perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc.In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets.In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user's perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.
ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

Underwater imagery and transmission possess numerous challenges like lower signal bandwidth, slower data transmission bit rates, Noise, underwater blue/green light haze etc.These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques.Due to the presence of blue/green light in underwater imagery, shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region.This method is proposed to efficiently deploy an Extreme Learning Machine (ELM) model-based shape adaptive Discrete Cosine Transformation (DCT) for underwater images.Underwater color image restoration techniques based on veiling light estimation and restoration of images followed by Saliency map estimation based on Gray Level Cooccurrence Matrix (GLCM) features are explained.An ELM network is modeled which takes two parameters, signal strength and saliency value of the region to be compressed and level of compression (DCT coefficients and compression steps) are predicted by ELM.This method ensures lesser errors in the Region of Interest and a better trade-off between available signal strength and compression level.
Acquisition of L2 English spatial deixes by Arabic-speaking children

Deictic words are considered the earliest words which children acquire at the stage of two-word-utterance. However, mastering them like adults may take more time. This paper investigates how L2 children comprehend and produce English spatial deixis 'here', 'there', 'this', and 'that' by observing and documenting their responses and reactions in hide-and-seek game. It also aims to find out the children's obstacles in acquiring these words, such as proximity bias and egocentrism. The subjects are Arabic children of ages four, five, and six who acquire English as a second language in international schools in Riyadh, Saudi Arabia. They performed two types of tasks: comprehension task and production task. Both tasks contained two trials: same perspective and the different perspective. Based on the results, children did better in comprehending the spatial deixis than in producing them. Moreover, the results showed that there was no proximity bias happened with children in this study. In addition, the results of the two trials in both tasks illustrated that changing the deictic center improves with age. Although the study provides some significant results, there should be an increase in the number of the samples in order to make the results generalized.
Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Web services are provided as reusable software components in the services-oriented architecture.More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service (QoS) limitations.The workflow consists of tasks where many services can be considered for each task.Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial (NP)-hard problem.This work focuses on the Web Service Composition (WSC) problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey.The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs.It also addresses the Bat Algorithm (BA) shortcomings, such as the tradeoff among exploration and exploitation searching mechanisms, local optima, and convergence rate.The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism.An elitist mechanism is utilized to address the BA convergence rate.The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism.Several benchmark datasets are selected to evaluate the proposed bat algorithm's performance.The simulation results are estimated using the average fitness value, the standard deviation of the fitness value, and an average of the execution time and compared with four bat-inspired algorithms.It is observed from the simulation results that introduced enhancement obtains significant results.
A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially, a DL model is proposed for feature extraction.Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS.Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.
DesertWoZ: A Wizard of Oz Environment to Support the Design of Collaborative Conversational Agents

The use of conversational agents to support team-based training in virtual environments is a nascent area of research within the Computer Supported Collaborative Work (CSCW) and Conversational User Interface (CUI) communities. To support this research, we developed a Wizard of Oz (WoZ) system enabling experimenters to play the role of the conversational agent who must work with a team of three human participants to herd together a group of autonomous robots (target agents) within a virtual task environment. The experimenter can select from one of several tailored responses. The aim of this environment is to determine whether the identified responses facilitate effective team performance and to collect training data to build a conversational agent capable of engaging in team based virtual learning environments.
DAO-Analyzer: Exploring Activity and Participation in Blockchain Organizations

Decentralized Autonomous Organizations (DAOs) are a new kind of organization that relies on blockchain software to govern their projects. Typically, DAO members may put forward and vote on proposals. For instance these proposals may consist on someone doing some tasks in exchange for a share of the DAO crypto-funds. In recent times, DAOs have gained a remarkable adoption, and yet they are still understudied by the academic literature. In this work, we present a visual analytics tool to study DAO activity focusing on their participation and temporal evolution. Our tool will hopefully help to stimulate research on this new kind of online community and collaborative software.
Enabling Remote Hand Guidance in Video Calls Using Directional Force Illusion

In current video meetings, communicating positions and hand movements is often challenging, as the meeting participants need to rely on gestures and verbal explanations, resulting in inefficient video meetings. We propose a video meeting system that utilizes a novel directional force feedback method so that people can efficiently guide each other's hand without the need for lengthy verbal explanations. The force feedback method uses asymmetric vibrations to create the illusion of directional pulling forces, which makes it possible to be built as a small and lightweight device. We also developed an online meeting system that allows a user to simply click a location on the remote user's video to move their hand towards the target location.
Housie: A Multiplayer Game for Cybersecurity Training and Evaluation

The pandemic has forced the enterprise sector to work from home, resulting in social isolation and lack of social interactions among colleagues. Meanwhile, the situation also led to an increase in cybersecurity risks. We launched a multiplayer cybersecurity awareness game to bring our organization's employees together and simultaneously equip them with cybersecurity knowledge. Bingo, one of the most played multiplayer games, was modified within the context of cybersecurity to provide a learning and entertaining experience to the players. This paper showcases an online, multiplayer, serious game ‘Housie’, launched for the enterprise sector, that integrates fun, immersion, and learning into one package. We discuss our initial study with over 56,000 participants, which provided insights into the cybersecurity awareness of our organization's employees. Our study found that the multiplayer format was widely accepted and provided insights on certain cybersecurity topics that need in-depth training.