Reviewerly/exHarmony huggingface dataset:
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation

Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In this paper, we report our recent achievements in S2ST. Firstly, we build a S2ST Transformer baseline which outperforms the original Translatotron. Secondly, we utilize the external data by pseudo-labeling and obtain a new state-of-the-art result on the Fisher English-to-Spanish test set. Indeed, we exploit the pseudo data with a combination of popular techniques which are not trivial when applied to S2ST. Moreover, we evaluate our approach on both syntactically similar (Spanish-English) and distant (English-Chinese) language pairs. Our implementation is available at https://github.com/fengpeng-yue/speech-to-speech-translation.
How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before.
Differentially Private Speaker Anonymization

Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal data. Speaker anonymization aims to remove speaker information from a speech utterance while leaving its linguistic and prosodic attributes intact. State-of-the-art techniques operate by disentangling the speaker information (represented via a speaker embedding) from these attributes and re-synthesizing speech based on the speaker embedding of another speaker. Prior research in the privacy community has shown that anonymization often provides brittle privacy protection, even less so any provable guarantee. In this work, we show that disentanglement is indeed not perfect: linguistic and prosodic attributes still contain speaker information. We remove speaker information from these attributes by introducing differentially private feature extractors based on an autoencoder and an automatic speech recognizer, respectively, trained using noise layers. We plug these extractors in the state-of-the-art anonymization pipeline and generate, for the first time, private speech utterances with a provable upper bound on the speaker information they contain. We evaluate empirically the privacy and utility resulting from our differentially private speaker anonymization approach on the LibriSpeech data set. Experimental results show that the generated utterances retain very high utility for automatic speech recognition training and inference, while being much better protected against strong adversaries who leverage the full knowledge of the anonymization process to try to infer the speaker identity.
MLPro 1.0 - Standardized reinforcement learning and game theory in Python

Nowadays there are numerous powerful software packages available for most areas of machine learning (ML). These can be roughly divided into frameworks that solve detailed aspects of ML and those that pursue holistic approaches for one or two learning paradigms. For the implementation of own ML applications, several packages often have to be involved and integrated through individual coding. The latter aspect in particular makes it difficult for newcomers to get started. It also makes a comparison with other works difficult, if not impossible. Especially in the area of reinforcement learning (RL), there is a lack of frameworks that fully implement the current concepts up to multi-agents (MARL) and model-based agents (MBRL). For the related field of game theory (GT), there are hardly any packages available that aim to solve real-world applications. Here we would like to make a contribution and propose the new framework MLPro, which is designed for the holistic realization of hybrid ML applications across all learning paradigms. This is made possible by an additional base layer in which the fundamentals of ML (interaction, adaptation, training, hyperparameter optimization) are defined on an abstract level. In contrast, concrete learning paradigms are implemented in higher sub-frameworks that build on the conventions of this additional base layer. This ensures a high degree of standardization and functional recombinability. Proven concepts and algorithms of existing frameworks can still be used. The first version of MLPro includes sub-frameworks for RL and cooperative GT.
Hardening attack surfaces with formally proven binary format parsers

With an eye toward performance, interoperability, or legacy concerns, low-level system software often must parse binary encoded data formats. Few tools are available for this task, especially since the formats involve a mixture of arithmetic and data dependence, beyond what can be handled by typical parser generators. As such, parsers are written by hand in languages like C, with inevitable errors leading to security vulnerabilities.
Multi-criteria group decision-making with cloud model and TOPSIS for alternative selection under uncertainty

A novel hybrid soft computing approach that integrates the cloud model and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is developed to deal with multi-criteria decision-making (MCDM) problems under uncertainty. Cloud models are used to derive linguistic concepts in numerical measures with the consideration of uncertainty. TOPSIS is used to determine the ranking of various alternatives with the evaluation matrices processed by the cloud model. The Monte Carlo simulation is implemented to model the uncertainty underlying the characterization of input factors and obtain their global sensitivities. An evaluation system with 16 criteria is formulated to discover the optimal alternative for tunnel excavation. A realistic tunnel section case in China is selected to demonstrate the adaptability and significance of the proposed approach. Results imply that (1) among the 5 candidate tunneling methods, the shield tunnel boring machine (TBM) is determined as the optimal method to excavate the tunnel section in the case; (2) the inconsistency in results occurs during the simulation in the presence of uncertainty, where there is 13% probability that soft-rock TBM will rank first in the case study; (3) with the increase of Entropy in cloud models, the degree of inconsistency will grow significantly. The developed approach can be used as a decision tool to provide insights into a better understanding of multi-source information fusion for alternative selection uncertainty with variations in the simulation and sensitivity analysis.
Random motion blur for optical image encryption

We present a compact optical encryption scheme by using a continuous-random-motion blurring model in an optical imaging system. Image encryption is performed by additive motion blur effects with continuous and random shifts of a camera. Real-time random phase modulation can be achieved without the use of random phase mask. Storage of the key is more convenient, which only requires parameters of motion. In addition, modulation characteristics are different from the traditional encryption schemes. On the premise of high security, modulation space is broadened, and flexibility of encryption is further improved. Simulations and experiments verify the validity of the motion blur-based crypto-system and demonstrate its security under several attacks. This novel method will be significant for the practical applications in the field of optical information security.
On Efficient and Secure Code-based Masking: A Pragmatic Evaluation

Code-based masking is a highly generalized type of masking schemes, which can be instantiated into specific cases by assigning different encoders. It captivates by its side-channel resistance against higher-order attacks and the potential to withstand fault injection attacks. However, similar to other algebraically-involved masking schemes, code-based masking is also burdened with expensive computational overhead. To mitigate such cost and make it efficient, we contribute to several improvements to the original scheme proposed by Wang et al. in TCHES 2020. Specifically, we devise a computationally friendly encoder and accordingly accelerate masked gadgets to leverage efficient implementations. In addition, we highlight that the amortization technique introduced by Wang et al. does not always lead to efficient implementations as expected, but actually decreases the efficiency in some cases.From the perspective of practical security, we carry out an extensive evaluation of the concrete security of code-based masking in the real world. On one hand, we select three representative variations of code-based masking as targets for an extensive evaluation. On the other hand, we aim at security assessment of both encoding and computations to investigate whether the state-of-the-art computational framework for code-based masking reaches the security of the corresponding encoding. By leveraging both leakage assessment tool and side-channel attacks, we verify the existence of “security order amplification” in practice and validate the reliability of the leakage quantification method proposed by Cheng et al. in TCHES 2021. In addition, we also study the security decrease caused by the “cost amortization” technique and redundancy of code-based masking. We identify a security bottleneck in the gadgets computations which limits the whole masked implementation. To the best of our knowledge, this is the first time that allows us to narrow down the gap between the theoretical security order under the probing model (sometimes with simulation experiments) and the concrete side-channel security level of protected implementations by code-based masking in practice.
Lightweight approach for malicious domain detection using machine learning

The web-based attacks use the vulnerabilities of the end users and their system and perform malicious activities such as stealing sensitive information, injecting malwares, redirecting to malicious sites without their knowledge.Malicious website links are spread through social media posts, emails and messages.The victim can be an individual or an organization and it creates huge money loss every year.Recent Internet Security report states that 83 % of systems in the internet are infected by the malware during the last 12 months due to the users who do not aware of the malicious URL (Uniform Resource Locators) and its impacts.There are some methods to detect and prevent the access malicious domain name in the internet.Blacklist-based approaches, heuristic-based methods, and machine/deep learning-based methods are the three categories.This study provides a machine learning-based lightweight solution to classify malicious domain names.Most of the existing research work is focused on increasing the number of features for better classification accuracy.But the proposed approach uses fewer number of features which include lexical, content based, bag of words, popularity features for malicious domain classification.Result of the experiment shows that the proposed approach performs better than the existing one.
Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node

The photonics platform has been considered increasingly promising for neuromorphic computing, due to its potential in providing low latency and energy efficient large-scale parallel connectivity. Phase change materials (PCMs) have been recently employed to introduce all-optical non-volatile memory in integrated photonic circuits, especially finding application as non-volatile weighting element in photonic artificial neural networks. Interestingly, these weighting elements can potentially be used as building blocks for large-scale networks that can autonomously adapt to their input, i.e. presenting the property of plasticity, similarly to the biological brain. In this work, we develop a computationally efficient dynamical model of a silicon ring resonator (RR) enhanced by a phase change material, namely Ge2Sb2Te5 (GST). We do so starting from two existing dynamical models (of a silicon RR and of a GST thin film on a straight silicon waveguide), but extending the optical equations to properly account for the high absorption and asymmetry in the ring due to the phase change material. Our model accounts for silicon nonlinear effects due to free carriers and temperature, as well as for the phase change of GST, whose energy efficiency and optical contrast can be enhanced by the RR resonant behaviour. We also restructure the optical equations so that the model can be efficiently employed in a modular way within a commercial software for system-level photonics simulations. Moreover, exploiting the developed model, we explore several design parameters and show that both speed and energy efficiency of memory operations can be enhanced by factors from six to ten. Also, we show that the achievable optical contrast due to GST phase change can be increased by more than a factor ten by leveraging the resonant properties of the RR, at the expense of higher optical loss. Finally, by exploiting the nonlinear dynamics arising in silicon RR networks, we show that a strong contrast is achievable while preserving energy efficiency.
DARTSRepair: Core-failure-set guided DARTS for network robustness to common corruptions

Network architecture search (NAS), in particular the differentiable architecture search (DARTS) method, has shown a great power to learn excellent model architectures on the specific dataset of interest. In contrast to using a fixed dataset, in this work, we focus on a different but important scenario for NAS: how to refine a deployed network’s model architecture to enhance its robustness with the guidance of a few collected and misclassified examples that are degraded by some real-world unknown corruptions having a specific pattern (e.g., noise, blur, etc..). To this end, we first conduct an empirical study to validate that the model architectures can be definitely related to the corruption patterns. Surprisingly, by just adding a few corrupted and misclassified examples (e.g., 103 examples) to the clean training dataset (e.g., 5.0×104 examples), we can refine the model architecture and enhance the robustness significantly. To make it more practical, the key problem, i.e., how to select the proper failure examples for the effective NAS guidance, should be carefully investigated. Then, we propose a novel core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture. We use our method for DARTS-refined DNNs on the clean as well as 15 corruptions with the guidance of four specific real-world corruptions. Compared with the state-of-the-art NAS as well as data-augmentation-based enhancement methods, our final method can achieve higher accuracy on both corrupted datasets and the original clean dataset. On some of the corruption patterns, we can achieve as high as over 45% absolute accuracy improvements.
Detecting hydrocarbon micro-seepage and related contamination, probable prospect areas, deduced from a comparative analysis of multispectral and hyperspectral satellite images

Detecting mineralogical alteration by hydrocarbon micro-seepage from trap to surface across the deformed structure, through Comparative analysis study of hyperspectral image (EO-1, Hyperion), and multispectral (landsat7 and Advanced land imager (Ali) to map soil alteration by petroleum seepage with applying remote sensing techniques as (band ratios, supervised, SAM classification, and hydrocarbon detection and index (HD and HI) calculation to detect the potentiality of hydrocarbon seepage in the area. The type of spectral resolution accuracy available for each space imaging platform can be used to choose which is best in determining the target and whether exploration yields oil. Hyperspectral remote sensing data are integrated with a GIS framework Weighted Sum (Spatial Analyst) classified hydrocarbon seepage prospects into five potential zones very good, good, intermediate, poor, and very poor probable potentiality. The spatial lithologic carbonaceous alteration appears closely coincident along reactivated leaking faults that cause hydrocarbon micro-seepage and altered surface lithology and mineralogical cement. The hyperspectral images have proven their worth in identifying and studying hydrocarbon leaks and the resulting environmental pollution and mineral alteration, as well as use micro-seeps as a pathfinder to locations of new oil explorations discoveries that can use as a pre-drill prediction of hydrocarbon occurrence and detected a prospect area for hydrocarbon drill.
Training Technologies as a Means of Communicative Competences Development of Prejudicial Inquiry Agencies’ Investigators

The communicative component of the investigator’s professional activities is a dominant one, as it demonstrates the employee’s ability to organize work on the basis of professionally balanced communication with the objects of interaction. The stage of obtaining higher education in institutions with specific learning environment is a sensitive period for the development of the main sociogenic structures of personality and the development of professional identity of future professionals. This period is characterised by a purposeful mastering of the system of professional knowledge and skills, formation of a holistic view of the professional community, which results in awareness of professional readiness. The use of training technology in the training of investigators helps to increase the efficiency of the professional training process, based on the basic principles of personality-oriented education. The aim of our research is to study the process of formation of components of communicative competence in future investigators of prejudicial inquiry agencies with the help of training technologies. Appropriate psychodiagnostic tools were used to solve this purpose, which was tested during the training of specialists for prejudicial inquiry agencies. Most of the respondents stated the existence of satisfaction with participating in the training courses, awareness of the importance of its informativeness and practical orientation. Post-training testing emphasized the participants’ rethinking of the value of interpersonal relations; growing desire to help and show warmth and friendliness in relations with others; the expediency and effectiveness of the use of cooperation and compromise in resolving conflict situations within a professional and personal environment.
AdIoTack: Quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks

Machine Learning-based techniques have shown success in cyber intelligence. However, they are increasingly becoming targets of sophisticated data-driven adversarial attacks resulting in misprediction, eroding their ability to detect threats on network devices. In this paper, we present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks, helping cybersecurity teams quantify and refine the resilience of their trained models for monitoring IoT networks. To assess the model for the worst-case scenario, AdIoTack performs white-box adversarial learning to launch successful volumetric attacks that decision tree ensemble models cannot flag. Our first contribution is to develop a white-box algorithm that takes a trained decision tree ensemble model and the profile of an intended network-based attack on a victim class as inputs. It then automatically generates recipes that specify certain packets on top of the indented attack packets (less than 15% overhead) that together can bypass the inference model unnoticed. We ensure that the generated attack instances are feasible for launching on IP networks and effective in their volumetric impact. Our second contribution develops a method to monitor the network behavior of connected devices actively, inject adversarial traffic (when feasible) on behalf of a victim IoT device, and successfully launch the intended attack. Our third contribution prototypes AdIoTack and validates its efficacy on a testbed consisting of a handful of real IoT devices monitored by a trained inference model. We demonstrate how the model detects all non-adversarial volumetric attacks on IoT devices while missing many adversarial ones. The fourth contribution develops systematic methods for applying patches to trained decision tree ensemble models, improving their resilience against adversarial volumetric attacks.
Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester

This paper presents a system for autonomously conducting precision harvesting missions using a legged harvester. Precision tree harvesting removes some trees selectively, while leaving neighboring trees intact. Our robot performs the challenging task of navigation and tree grabbing in a confined, GPS-denied forest environment. We propose strategies for mapping, localization, planning, and control and integrate them into a fully autonomous system. The mission starts with a human mapping the area of interest using a detachable, custom sensor module. Subsequently, a human expert selects the specific trees for harvesting. The sensor module is then mounted on the machine and used for localization within the created map. A planning algorithm searches for both an approach-pose and a path in a single path planning problem. We design a path-following controller exploiting the legged harvester’s capabilities for negotiating rough terrain. Upon reaching the approach-pose, the machine grabs a tree with a general-purpose gripper. Our system has been tested in both emulated and natural forest settings. To the best of our knowledge, ours is the first robot to demonstrate such a level of autonomy on a full-size, hydraulic machine operating in a realistic environment.