Reviewerly/exHarmony huggingface dataset:
Sensitivity as a Complexity Measure for Sequence Classification Tasks

Abstract We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a function, given a distribution over input sequences, quantifies the number of disjoint subsets of the input sequence that can each be individually changed to change the output. We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult. To that end, we show analytically that simple lexical classifiers can only express functions of bounded sensitivity, and we show empirically that low-sensitivity functions are easier to learn for LSTMs. We then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks, and that sensitivity predicts the performance both of simple lexical classifiers and of vanilla BiLSTMs without pretrained contextualized embeddings. Within a task, sensitivity predicts which inputs are hard for such simple models. Our results suggest that the success of massively pretrained contextual representations stems in part because they provide representations from which information can be extracted by low-sensitivity decoders.
Cryptography from sublinear-time average-case hardness of time-bounded Kolmogorov complexity

Let MKtP[s] be the set of strings x such that Kt(x) ≤ s(|x|), where Kt(x) denotes the t-bounded Kolmogorov complexity of the truthtable described by x. Our main theorem shows that for an appropriate notion of mild average-case hardness, for every ε>0, polynomial t(n) ≥ (1+ε)n, and every “nice” class F of super-polynomial functions, the following are equivalent: (i) the existence of some function T ∈ F such that T-hard one-way functions (OWF) exists (with non-uniform security); (ii) the existence of some function T ∈ F such that MKtP[T−1] is mildly average-case hard with respect to sublinear-time non-uniform algorithms (with running-time nδ for some 0<δ<1). For instance, existence of subexponentially-hard (resp. quasi-poly-nomially-hard) OWFs is equivalent to mild average-case hardness of MKtP[poly logn] (resp. MKtP[2O(√logn))]) w.r.t. sublinear-time non-uniform algorithms. We additionally note that if we want to deduce T-hard OWFs where security holds w.r.t. uniform T-time probabilistic attackers (i.e., uniformly-secure OWFs), it suffices to assume sublinear time hardness of MKtP w.r.t. uniform probabilistic sublinear-time attackers. We complement this result by proving lower bounds that come surprisingly close to what is required to unconditionally deduce the existence of (uniformly-secure) OWFs: MKtP[polylogn] is worst-case hard w.r.t. uniform probabilistic sublinear-time algorithms, and MKtP[n−logn] is mildly average-case hard for all O(t(n)/n3)-time deterministic algorithms.
Towards a Blockchain Database for Massive IoT Workloads

The Internet of Things (IoT) revolution has massively introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this vision paper we propose Triabase, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. As such, it does not store detailed records on a medium, like blockchains, which is fundamentally very slow due to the expensive verification process. We lay out the primitive architectural blocks of our design, the requirements and the inherent challenges. Triabase employs technical novelties in respect to its consensus protocol, namely the notion of Proof-of-Federated-Learning (PoFL). The Triabase prototype system is implemented in the Hyperledger Fabric blockchain framework, upon which encouraging preliminary findings have been drawn.
Multi-UAVs Task Assignment Based on Fully Adaptive Cross-Entropy Algorithm

Multi-UAVs task assignment is regarded as an important problem in the UAV research domain. To assign multi-UAVs tasks efficiently and accurately, a common method of assigning tasks is needed with resource constraints and precedence constraint. Then, we apply fully adaptive cross-entropy (FACE) algorithm to multi-UAVs task assignment, and the resources and the sequence required for three tasks are also taken into account. Through emphasizing the iterative principle of FACE algorithm and utilizing it to handle the multi-UAVs task assignment problem with constraints, the simulations validate that compared with the cross-entropy method and the particle swarm optimization algorithm, the FACE algorithm has fast convergence speed and the strength to solve large-scale allocation problems.
Digital Image Forensic Analysis using Hybrid Features

Copy Move Forgery (CMF) is an exceptional sort of image control technique in which any section of the input sample is copied into another piece of a similar image. Recently, several researchers proposed different techniques to deal with the authentication of images. However, still, there is a need for improvement both in terms of localization performance and time efficiency. In this paper, we presented a passive approach that is based on LDP with DCT for the CMF recognition. Initially, the image is transformed to YCbCr and then distributes into overlapping blocks. The features are extracted through LDP with DCT and after that similarity of the blocks is measured through Euclidean distance. The evaluation of our presented framework is performed on a challenging dataset namely CoMoFoD_Small_V2. The proposed method achieved a higher precision of 99% and a recall of 96% as compared to latest methods. Experiments show that our approach successfully detects the CMF parts and is more robust to post-processing operations.
Missing Value Imputation Method Using Separate Features Nearest Neighbors Algorithm

Missing value imputation is a problem often meet when working with medical and biometric data sets. Prior to working on these datasets, missing values have to be eliminated. It could be done by imputing estimated values. However, imputation should not bias data, nor alter the class balance. This paper presents an innovative approach to the problem of imputation of missing values in the training data for the classification. Method uses the k-NN classifier on a separate features to impute missing values. The unique approach used in this method allows using data from incomplete vectors to impute another incomplete vectors, unlike in conventional methods, where only complete vectors could be used in the imputation process. The paper also describes a test protocol, where the Cross Validation with a Set Substitution method is used as an evaluation tool for scoring missing value imputation methods.
Effects of virtual reality application on skill learning for optical‐fibre fusion splicing

Abstract This study explored the effects of virtual reality (VR) application on the process and outcome of skill learning. The participants consisted of 60 new employees in the customer network cable service division of a telecommunications company, and the study focused on the learning of optical‐fibre fusion splicing skills. The skill‐learning test results revealed the following: (a) the skill‐learning effect associated with VR application was greater than that of conventional lecturing and demonstration approaches; (b) VR application had a considerable negative effect on the perception level of skill learning but a moderately positive effect on adaptation and origination levels; and (c) in the VR operation stage of learning, the behavioural transition from the cognitive phase to the associative phase was the most prominent. Practitioner notes What is already known about this topic Virtual reality (VR) can transmit complex knowledge Virtual reality (VR) can facilitate the skill learning What this paper adds How to apply VR to skill learning Effects of VR teaching on steps of skill learning process and learning levels Effects of VR teaching on stages of skill learning Implications for practice and/or policy Using VR teaching to improve skill learning effects Using VR teaching to improve skill learning stages and levels.
On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results

Self-sufficient, robotic spacecraft require estimates of their hardware health state in order to project future system state and plan actions toward achieving mission goals. In this paper, we report on integration of a Model-Based Fault Diagnosis (MBFD) model and reasoning engine into flight software leveraging the Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) mission, including test results against captured flight data using the ASTERIA system testbed. Our effort integrated the Model-based Off-Nominal State Identification and Detection (MONSID) model-based reasoning system, developed by Okean Solutions, into ASTERIA flight software using the F Prime software framework. The MONSID engine was supplied with a model of the Blue Canyon Technologies XACT attitude control system (ACS) and tested against flight data and seeded fault tests. While we were unable to conduct an on-board experiment due to the premature loss of ASTERIA, our effort proved the feasibility of on-board model-based fault management, demonstrating reliable and accurate diagnosis using captured data, and further supporting a closed-loop spacecraft autonomy demonstration including autonomous navigation in off-nominal conditions.
Development of Evaluating Methods for Passenger’s Motion Sickness in Real Driving Environment

With the commercialization of autonomous driving technology and diversification of how passengers spend their time, it is concerned that a mismatch between somatosensory and visual information induces motion sickness. However, no effective quantitative method using physiological indices in driving conditions of a vehicle has been found out. The objective of this study is to develop a robust method for quantitative evaluation in real driving environment. As a result of inducing motion sickness of a back passenger by driving a vehicle on a test course, changes of forehead humidity, which reflects the amount of sweating, increased significantly. The result indicates forehead humidity is an effective index for quantifying motion sickness in real driving environment.
The Use of Augmented Reality to Expand the Experience of Museum Visitors

This chapter is devoted to augmented reality in the museum context. Augmented reality has been regarded as a way to enhance the real-world environment using computer-generated perceptual information. It is a tool that museums should explore more to enhance the visitor experience. The aim of this chapter is to go further in understanding the use of augmented reality technology in museums. The objectives are (1) to give an overview of the meaning of augmented reality in tourism, (2) to provide examples of the use of augmented reality in museums, and (3) to offer suggestions for future research in the field. The results are the suggestions for future research and the contributions for academics and practitioners.
Mobile Physical-Layer Authentication Using Channel State Information and Conditional Recurrent Neural Networks

Channel state information offers a unique characteristic that can be used for authenticating devices at the physical layer. The quickly changing response of the mobile channel presents a challenge for identifying trusted transmitters. In this paper, we propose the use of recurrent neural networks to predict these changes in order to make an authentication decision. Using previous channel response measurements, we condition the output of the network and estimate future channel responses. This work presents a novel method for physical layer authentication using two variations of a conditional generative adversarial network (CGAN) and evaluates the CGAN accuracy against networks using long short-term memory (LSTM) and gated recurrent unit (GRU) cells. Performance evaluation reveals promising results for the GRU-enabled CGAN achieving 96.2% accuracy as well as the LSTM-enabled CGAN reaching 90.9%, compared with the GRU and LSTM standalone networks, scoring 98.1% and 88.9%, respectively.
Small Object Detection of Table Tennis Based on Deep Learning Network

According to the characteristics of the visual system of table tennis robot and the fast movement speed of table tennis, this paper focuses on balancing the speed and accuracy of small object detection based on deep learning network. We propose a deep learning network which is more suitable for small target detection, improving the detection accuracy while ensuring the detection speed to meet the real-time match. Firstly, we constructed a dataset including 20000 table tennis images. Then, we use convolutional neural network with residual connection to extract image features and construct the feature pyramid to increase the performance of network for small object detection. The feature map with rich semantic information in the upper layer is fused with the layer with rich object position information in the lower layer. We propose a new data augmentation method. During training the table tennis target on each picture is copied three times, which greatly increases the ability of network learning the feature of small target. Experimental results show that our model proposed in this paper can improve the accuracy and speed of table tennis detection, and bring great significance to the design and application of table tennis visual system.
Neuromorphic Time-Multiplexed Reservoir Computing With On-the-Fly Weight Generation for Edge Devices

The human brain has evolved to perform complex and computationally expensive cognitive tasks, such as audio-visual perception and object detection, with ease. For instance, the brain can recognize speech in different dialects and perform other cognitive tasks, such as attention, memory, and motor control, with just 20 W of power consumption. Taking inspiration from neural systems, we propose a low-power neuromorphic hardware architecture to perform classification on temporal data at the edge. The proposed architecture uses a neuromorphic cochlea model for feature extraction and reservoir computing (RC) framework as a classifier. In the proposed hardware architecture, the RC framework is modified for on-the-fly generation of reservoir connectivity, along with binary feedforward and reservoir weights. Also, a large reservoir is split into multiple small reservoirs for efficient use of hardware resources. These modifications reduce the computational and memory resources required, thereby resulting in a lower power budget. The proposed classifier is validated for speech and human activity recognition (HAR) tasks. We have prototyped our hardware architecture using Intel's cyclone-10 low-power series field-programmable gate array (FPGA), consuming only 4790 logic elements (LEs) and 34.9-kB memory, making it a perfect candidate for edge computing applications. Moreover, we have implemented a complete system for speech recognition with the feature extraction block (cochlea model) and the proposed classifier, utilizing 15 532 LEs and 38.4-kB memory. By using the proposed idea of multiple small reservoirs along with on-the-fly generation of reservoir binary weights, our architecture can reduce the power consumption and memory requirement by order of magnitude compared to existing FPGA models for speech recognition tasks with similar complexity.
Low-Cost Hiding of the Query Pattern

Several attacks have shown that the leakage from the access pattern in searchable encryption is dangerous. Attacks exploiting leakage from the query pattern are as dangerous, but less explored. While there are known, lightweight countermeasures to hide information in the access pattern by padding the ciphertexts, the same is not true for the query pattern. Oblivious RAM hides the query patterns, but requires a logarithmic overhead in the size of the database and hence will become even slower as data grows. In this paper we present a query smoothing algorithm to hide the frequency information in the query pattern of searchable encryption schemes by introducing fake queries. Our method only introduces a constant overhead of 7 to 13 fake queries per real query in our experiments. Furthermore, we show that our query smoothing algorithm can also be applied to range-searchable encryption schemes and then prevents all recent plaintext recovery attacks.
Backdoor smoothing: Demystifying backdoor attacks on deep neural networks

Backdoor attacks mislead machine-learning models to output an attacker-specified class when presented a specific trigger at test time. These attacks require poisoning the training data to compromise the learning algorithm, e.g., by injecting poisoning samples containing the trigger into the training set, along with the desired class label. Despite the increasing number of studies on backdoor attacks and defenses, the underlying factors affecting the success of backdoor attacks, along with their impact on the learning algorithm, are not yet well understood. In this work, we aim to shed light on this issue by unveiling that backdoor attacks induce a smoother decision function around the triggered samples – a phenomenon which we refer to as backdoor smoothing. To quantify backdoor smoothing, we define a measure that evaluates the uncertainty associated to the predictions of a classifier around the input samples. Our experiments show that smoothness increases when the trigger is added to the input samples, and that this phenomenon is more pronounced for more successful attacks. We also provide preliminary evidence that backdoor triggers are not the only smoothing-inducing patterns, but that also other artificial patterns can be detected by our approach, paving the way towards understanding the limitations of current defenses and designing novel ones.
Cryptocurrency and Corruption: Auditing with Blockchain

Technological developments have played a major role in supporting all sectors, globally. These developments have impacted sectors ranging from sports and healthcare to transport. Technology has reduced the level of fraud and dramatically increased liability at all levels. Financial tracking using technology can help reduce public sector corruption amounting to around $2 trillion, or around 2% of global GDP. The better the financial monitoring systems, the better the chances of stronger economic development. Cryptocurrency uses cryptography to ensure transaction security by identifying the users of the currency in each transaction as opposed to traditional payment methods such as banknotes and coins. Although most cryptocurrencies are anonymous and it is not possible to identify the user, it is technologically possible to add an identification number that does not make the crypto anonymous. Governments can use blockchain technology to their advantage not only to facilitate payments, but also to keep them authorized and secure. Cryptocurrencies are a much more efficient payment method than other existing methods. They have become even more reliable and efficient. This chapter maintains that not only can crypto make payments easier, but it can also prove to be a useful way to reduce fraud and corruption around the world. This can reduce execution costs and, based on information already available on transactions, the effectiveness and efficiency of spending can be measured.
Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.
Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.
Encrypted Malware Traffic Detection Using TLS Features and Random Forest

Encrypted malware traffic detection has been an essential issue in network security. With more and more malwares using TLS to encrypt their traffic, most payload-based methods can’t work well as before. Several works have been conducted in this field. To improve performance, this paper analyzes and proposes several new TLS features to analyze the characteristics of encrypted malwares and further detects their traffic. The analysis shows that during the TLS sessions, most malwares use less TLS extensions and cipher suites and often use self-signed certificates. Based on the result of analysis, this paper proposes to use random forest to identify TLS encrypted malware traffic. Experimental results show that when combined with proposed features, the TLS features can achieve higher recall rate.