Reviewerly/exHarmony huggingface dataset:
A Study on the Feasibility to Detect Hate Speech in Swedish

Hate speech in digital environments is becoming a societal challenge. To deal with the problem, techniques that automatically detect hate speech have been developed by social media companies as well as researchers. Hate can be expressed in many different ways, which makes it difficult to detect automatically using algorithms. Also, how hate is expressed depends heavily on the language. The effectiveness of automatic detection techniques is still to be improved in many languages. In this paper, we attempt to detect hate speech in Swedish using machine learning. We compare different pre-trained language models that are fine-tuned on a corpus of hateful comments. To examine how well our models would work in a real scenario, we used a set of randomly selected comments from a Swedish discussion forum. The results showed that using pre-trained language models provides a better result than using a baseline SVM model, but it also reveals that detecting hate speech in the wild is challenge that need more research.
Observing a topological transition in weak-measurement-induced geometric phases

Measurement plays a quintessential role in the control of quantum systems. Beyond initialization and readout which pertain to projective measurements, weak measurements, in particular through their back action on the system, may enable various levels of coherent control. The latter ranges from observing quantum trajectories to state dragging and steering. Furthermore, just like the adiabatic evolution of quantum states that is known to induce the Berry phase, sequential weak measurements may lead to path-dependent geometric phases. Here we measure the geometric phases induced by sequences of weak measurements and demonstrate a topological transition in the geometric phase controlled by measurement strength. This connection between weak measurement-induced quantum dynamics and topological transitions reveals subtle topological features in measurement-based manipulation of quantum systems. Our protocol could be implemented for classes of operations (e.g., braiding) that are topological in nature. Furthermore, our results open new horizons for measurement-enabled quantum control of many-body topological states.
Blockchain Implementation for Internet of Things Applications

Third-party agencies including banks maintain a centralized database to protect our digital assets, but the risk of being hacked, being compromised, or manipulation cannot be overlooked. This brings in the idea of distributed consensus and anonymity along with safety and perseverance of available data. Blockchain, the technology powering the most popular cryptocurrency, bitcoin, promises to assure security and privacy of data. But its huge carbon footprint hinders to its implementation to resource-constrained Internet of Things (IoT) devices. On the other hand, cost, value, and validity of IoT applications do not make it feasible for the standard blockchain implementation. This forces for some simplification in the most energy-consuming component (the mining process), thus allowing all the computations on IoT devices still ensuring the required level of security for given applications. The work in this chapter proposes a modified energy-efficient technique for computation of proof of work, followed by blockchain implementation on resource-constrained IoT for ambient living or ambient assisted living applications for elderly care. This application gathers data from different sensors on a single-board computer for a big hall, which mines for up to a convincing level of difficulty. Later, the computation task is appointed to few compute-efficient machines to reduce the computation time. The emulation results show that the proposed solution is energy efficient and more suitable to resource-limited IoT applications as compared to standard blockchain implementation. The chapter concludes with answer to potential research questions aroused in recent works in the domain on efficacy of blockchain for IoT.
Design and Implementation of the Digital Education Resources Authentication System Based on Blockchain

With the approaching of the digital age, digital education resources bring convenience to people because they are prone to duplicate and spread, but there are also some disadvantages in online education service transactions. The security issues of digital education resources and the phenomenon of illegal distribution seriously damage the rights and interests of the owners of digital education resources. Based on this, in this paper, we proposed a blockchain-based digital education resources authentication system which uses the decentralized, tamper-resistant features of blockchain technology to store the digital education resources information in the form of a secret text. In the system, there is no need to require the participation of any third-party institution. The system proposed in this study will greatly protect the rights and interests of the owner of digital education resources.
Multi-adversarial Variational Autoencoder Networks

The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images. Combining the power of these two generative models, we introduce a novel network architecture, Multi-Adversarial Variational autoEncoder Networks (MAVENs), which incorporate an ensemble of discriminators in a combined VAE-GAN network, with simultaneous adversarial learning and variational inference. We apply MAVENs to the generation of synthetic images and propose a new distribution measure to evaluate the quality of the generated images. Our experimental results using the computer vision datasets SVHN and CIFAR-10 demonstrate competitive performance against state-of-the-art semi-supervised models both in image generation and classification tasks.
A CFI Countermeasure Against GOT Overwrite Attacks

In the Unix-like system, the Global Offset Table (GOT) overwrite attack is a long-lasting control flow hijacking attack.The attack, by leveraging the dynamic symbol binding mechanism, overwrites any GOT entry into the attacker's target address to take the execution flow on the library function call.Recently, Full Relro (Relocation Read only), which arranges the GOT section as read-only at program startup, is regarded as most useful against the threat.However, it entails nontrivial loading overhead and is not applicable to libraries.Furthermore, many software packages are currently distributed without Full Relro.As a result, programs are still exposed to the risk of GOT attacks.In this paper, we propose a CFI-based protection scheme against the GOT overwrite attack.Using dynamically bound function symbols as branch identifiers, the scheme secures inter-module function calls on PLT (Procedure Linkage Table ) effectively with little performance overhead.Our LLVM based implementation and evaluation on binutils-gdb show that the branch protection scheme is difficult to bypass, fast, and compatible with existing library programs.
Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds

As ubiquitous as it is, the Internet has spawned a slew of products that have forever changed the way one thinks of society and politics. This article proposes a model to predict chances of a political party winning based on data collected from Twitter microblogging website, because it is the most popular microblogging platform in the world. Using unsupervised topic modeling and the NRC Emotion Lexicon, the authors demonstrate how it is possible to predict results by analyzing eight types of emotions expressed by users on Twitter. To prove the results based on empirical analysis, the authors examine the Twitter messages posted during 14th Gujarat Legislative Assembly election, 2017. Implementing two unsupervised clustering methods of K-means and Latent Dirichlet Allocation, this research shows how the proposed model is able to examine and summarize observations based on underlying semantic structures of messages posted on Twitter. These two well-known unsupervised clustering methods provide a firm base for the proposed model to enable streamlining of decision-making processes objectively.
Generation of two different polarization-entangled biphoton states in a potassium titanyl phosphate waveguide device with multiple periodic poling

In this paper, we propose a single integrated waveguide device to generate two different polarization-entangled Bell states involving nondegenerate photon pairs (with the signal photons being emitted in the telecom $C$ band) by utilizing type-0, type-I, and type-II quasi-phase-matching (QPM). In this context, we have studied ppKTP (periodically-poled potassium titanyl phosphate) in detail. To obtain polarization entanglement of the emitted photons, we have used two different situations, two cascaded QPM gratings (of the same length) to obtain $|{\ensuremath{\psi}}^{+}\ensuremath{\rangle}$, and two separate QPM gratings (of different lengths) to obtain $|{\ensuremath{\phi}}^{+}\ensuremath{\rangle}$. In such a scenario, maximal entanglement can be quantified in terms of overlap bandwidths of the two phase-matched processes.
Fall detection and recognition based on GCN and 2D Pose

In this paper, based on the motion properties of human skeleton, the neural network model based on graph convolution network (GCN) and 2D pose is introduced to carry out deep learning and classification of target actions, so as to detect falls. The experimental results show that our method is superior to the existing methods in two-dimensional pose, and the accuracy of the two aspects is not very different in 3D pose. However, we use GPU acceleration for testing on ubuntu platform with an average speed of 25fps, which basically meets the requirements of real-time engineering calculation and is easier to deploy to the real environment than 3D pose.
Deep Learning Based Automated Attendance System

A significant portion of the time allocated to a faculty for teaching purposes is consumed on the task of taking attendance of the students. This is an issue because it takes the valuable time of teachers which could be spent on more productive tasks such as teaching and interacting with students. In excess to the increase in chaos and loss of decorum in the classroom environment, the presence of proxy attendance also plagues the existing method of manual attendance keeping. To counter these issues, this paper proposed the Deep Learning Assisted Attendance System (DPAAS); which keeps track of students attending a particular class with the help of a continuous stream of pictures captured from a video streaming device located inside a classroom connected to the remote server. The proposed DPAAS method reduces the amount of time spent by the faculty on taking attendance, and leads to a reduction in chaos inside a classroom. DPASS is proposed handles the issues in existing systems such as multi-class identification for multiple individuals in a classroom, occlusion and differing light scenarios. The DPAAS methodology compares the results of the state of art algorithms, and uses the best fit architecture which provides the lowest false rate on evaluation. There is no need of user interaction in the proposed DPAAS. Experimental results show that the proposed DPAAS method gives 94.66% accuracy which is better than the other existing methods.
Deep Learning Based Loitering Detection System Using Multi-Camera Video Surveillance Network

A deep-learning-based Loitering Detection System (LDS) with re-identification (ReID) capability over a multicamera network is proposed. The proposed LDS is mainly comprising of object detection and tracking, loitering detection, feature extraction, camera switching, and re-identification of the loiterer. The person is detected using You Only Look Once (YOLOv3) and tracked using Simple Online Real-time Tracking with a deep association matrix (DeepSORT). From the trajectory analysis, once the time and displacements thresholds are satisfied, the person is treated as a loiterer. When the loiterer moves one camera to another, then the algorithm is switched to the appropriate camera feed as per the proposed camera switching algorithm to minimize the computational cost. Subsequently, the loiterer is reidentified in the switched camera feed by comparing the features of the loiterer extracted by the MobileNets with that of the other detected persons based on the triplet loss criteria. The proposed system provides an enhanced accuracy of 96 % on average fps of 33 (without ReID) and 81.5 % at average fps of 30 (with ReID).
Approximate Deep Network Embedding for Mining Large-Scale Graphs

In the Big Data era, we are witnessing the flood of big graph data in terms of volume, variety, and velocity. The use of online social media and online shopping sites has provided access to a huge volume of interactions among information entities and objects. Scaling compute-intensive graph analysis applications on huge graphs with millions or billions of vertices and edges is widely recognized as a challenging big data research problem. A lot of research efforts in network embedding aim to learn low-dimensional representation of big graphs, enable easy integration with existing graph mining algorithms, and thus allow to achieve acceptable quality of big graph analysis on network embedding results with superior efficiency and scalability. However, how to enhance network embedding itself in terms of both efficiency and scalability is still an open problem. We are still short of efficient and scalable network embedding techniques to scale themselves on big graphs with millions or billions of vertices and edges, with the awareness of the intrinsic global and local characteristics of graph data. Most network embedding techniques exploit shallow-structured architectures, and thus lead to sub-optimal network representations. We also see lots of potential to utilize approximation theories and deep learning techniques to elevate both efficiency and scalability. In order to promote big network embedding from theoretical points of view, by representing graph data in deep learning architectures, we develop a suite of competitive learning-based approximate deep network embedding techniques that are able to leverage both efficiency and scalability of network embedding while preserving the computational utility with three major components. First, we propose a dynamic competitive learning-based algorithm to combine global network embedding and local network embedding into a unified model to utilize the advantages of both techniques. Second, we develop a network embedding-based algorithm with the optimization of competitive learning to tightly integrate vertex clustering and edge clustering by mutually enhancing each other. Third but last, we explore the opportunities of competitive learning and ranking for the optimal top-K neuron selection in the learning process of deep network embedding, in order to achieve a good balance between effectiveness and efficiency. The approximate deep network embedding approaches allow the deep learning model themselves to deactivate those insignificant neurons in the hidden layers through competitive learning, and thus reduce the computational cost of the feedforward pass and the back propagation.
A Self-Guiding Tool to Conduct Research With Embodiment Technologies Responsibly

The extension of the sense of self to the avatar during experiences of avatar embodiment requires thorough ethical and legal consideration, especially in light of potential scenarios involving physical or psychological harm caused to, or by, embodied avatars. We provide researchers and developers working in the field of virtual and robot embodiment technologies with a self-guidance tool based on the principles of Responsible Research and Innovation (RRI). This tool will help them engage in ethical and responsible research and innovation in the area of embodiment technologies in a way that guarantees all the rights of the embodied users and their interactors, including safety, privacy, autonomy, and dignity.
Speech Reveals Future Risk of Developing Dementia: Predictive Dementia Screening from Biographic Interviews

Alzheimer's disease is a progressive incurable condition for which the success of any symptomatic therapy depends crucially on the starting time. Ideally it starts before the disease has caused any cognitive impairments. Our work aims at developing speech-based dementia screening methods that detect dementia as early as possible. Here, we aim to predict the outbreak even before clinical screening tests can diagnose the disease. Using the longitudinal ILSE study, we automatically extract features from biographic interviews and predict the development of dementia 5 and 12 years into the future. Our prediction system achieves results of 73.3% and 75.7% unweighted average recall (UAR), respectively, which clearly outperform a prediction based on prior diagnoses or disease prevalence. Thus, the automated analysis of spoken interviews offers a highly effective prediction procedure that allows for easy-to-use, cost-effective casual testing.
Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications
A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling

In this paper, a multiple-input multiple-output reservoir computing (RC) system is proposed, which is composed of multiple nonlinear nodes (Mach–Zehnder modulators) and multiple mutual-coupling loops of optoelectronic delay lines. Each input signal is added into every mutual-coupling loop to implement the simultaneous recognition of multiple route signals, which results in the signal processing speed improving and the number of routes increasing. As an example, the four-route input and four-route output RC is simultaneously realized by numerical simulations. The results show that this type of RC system can successfully recognize the four-route optical packet headers with 3-bit, 8-bit, 16-bit, and 32-bit, and four-route independent digital speeches. When the white noise is added to the signals such that the signal-to-noise ratio (SNR) of the optical packet headers and the digital speeches are 35 dB and 20 dB respectively, the normalized root mean square errors (NRMSEs) of the signal recognition are all close to 0.1. The word error rates (WERs) of the optical packet header recognition are 0%. The WER of the digital speech recognition is 1.6%. The eight-route input and eight-route output RC is also numerically simulated. The recognition of the eight-route 3-bit optical packet headers is implemented. The parallel processing of multiple-route signals and the high recognition accuracy are implemented by this proposed system.
AADS: A Noise-Robust Anomaly Detection Framework for Industrial Control Systems

Deep Neural Networks are emerging as effective techniques to detect sophisticated cyber-attacks targeting Industrial Control Systems (ICSs). In general, these techniques focus on learning a “normal” behavior of the system, to be then able to label noteworthy deviations from it as anomalies. However, during operations, ICSs inevitably and continuously evolve their behavior, due to e.g., replacement of devices, workflow modifications, or other reasons. As a consequence, the quality of the anomaly detection process may be dramatically affected with a considerable amount of false alarms being generated. This paper presents AADS (Adaptive Anomaly Detection in industrial control Systems), a novel framework based on neural networks and greedy-algorithms that tailors the learning-based anomaly detection process to the changing nature of ICSs. AADS efficiently adapts a pre-trained model to learn new changes in the system behavior with a small number of data samples (i.e., time steps) and a few gradient updates. The performance of AADS is evaluated using the Secure Water Treatment (SWaT) dataset, and its sensitivity to additive noise is investigated. Our results show an increased detection rate compared to state of the art approaches, as well as more robustness to additive noise.
Power Saving and Secure Text Input for Commodity Smart Watches

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances.