Reviewerly/exHarmony huggingface dataset:
Towards Sound Forensic Arguments: Structured Argumentation Applied to Digital Forensics Practice

Digital forensic practitioners are increasingly facing examinations which are both complex in nature and structure. Throughout this process, during the examination and analysis phases, the practitioner is constantly drawing logical inferences which will be reflected in the reporting of results. Therefore, it is important to expose how all the elements of an investigation fit together to allow review and scrutiny, and to support associated parties to understand the components within it. This paper proposes the use of ‘Structured Argumentation’ as a valuable and flexible ingredient of the practitioners' thinking toolbox. It explores this approach using three case examples which allow discussion of the benefits and application of structured argumentation to real world contexts. We argue that, despite requiring a short learning curve, structured argumentation is a practical method which promotes accessibility of findings facilitating communication between technical and legal parties, peer review, logical reconstruction, jury interpretation, and error detection.
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation.

Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a single grapheme-based ASR model learned on 7 geographically proximal languages, using standard hybrid BLSTM-HMM acoustic models with lattice-free MMI objective. We build the single ASR grapheme set via taking the union over each language-specific grapheme set, and we find such multilingual graphemic hybrid ASR model can perform language-independent recognition on all 7 languages, and substantially outperform each monolingual ASR model. Secondly, we evaluate the efficacy of multiple data augmentation alternatives within language, as well as their complementarity with multilingual modeling. Overall, we show that the proposed multilingual graphemic hybrid ASR with various data augmentation can not only recognize any within training set languages, but also provide large ASR performance improvements.
Outsourced ciphertext-policy attribute-based encryption with partial policy hidden

Attribute-based encryption is an efficient and flexible fine-grained access control scheme. However, how to realize the attribute privacy concerns in the access policy and optimize the heavy computing overhead have been not adequately addressed. First, in view of the open-access policies formulated by data owners in the cloud environment and the linear growth of bilinear pairing operations with the number of attributes in the decryption process, a verifiable outsourced attribute-based encryption with partial policy hidden scheme is proposed, in which the attribute name of access policy can be sent while attribute value involving sensitive information can be hidden, so nobody can infer information from the access policy. Second, the bilinear pairing operation and modular power operation are outsourced to the cloud server, then users only need to perform constant exponential operation to decrypt. In addition, the proposed scheme is based on the composite order bilinear group and satisfies full secure under the standard model. Finally, compared with other schemes in term of function and performance, it shows that this scheme is more efficient and suitable for resource-constrained mobile devices in outsourcing environment.
SLED: Semi-supervised Locally-weighted Ensemble Detector

Concept drift detection refers to the process of detecting changes in the underlying distribution of data. Interest in the data stream mining community has increased, because of their role in improving the performance of online learning algorithms. Over the years, a myriad of drift detection methods have been proposed. However, most of these methods are single detectors, which usually work well only with a single type of drift. In this research, we propose a semi-supervised locally-weighted ensemble detector (SLED), where the relative performance among its base detectors is characterized by a set of weights learned in a semi-supervised manner. The aim of this technique is to effectively deal with both abrupt and gradual concept drifts. In our experiments, SLED is configured with ten well-known drift detectors. To evaluate the performance of SLED, we compare it with single detectors as well as state-of-the-art ensemble methods on both synthetic and real-world datasets using different performance measures. The experimental results show that SLED has fewer false positives, higher precision, and higher Matthews correlation coefficient while maintaining reasonably good performance for other measures.
Exploring Smart Agents for the Interaction with Multimodal Mediated Environments

After conversational agents have been made available to the broader public, we speculate that applying them as a mediator for adaptive environments reduces control complexity and increases user experience by providing a more natural interaction. We implemented and tested four agents, each of them differing in their system intelligence and input modality, as personal assistants for Mediated Atmospheres, an adaptive smart office prototype. They were evaluated in a user study ( N = 33 ) to collect subjective and objective measures. Results showed that a smartphone application was the most favorable system, followed by conversational text and voice agents that were perceived as being more engaging and intelligent than a non-conversational voice agent. Significant differences were observed between native and non-native speakers in both subjective and objective measures. Our findings reveal the potential of conversational agents for the interaction with adaptive environments to reduce work and information overload.
Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks

For the successful operation of smart home environments, it is important to know the state or activity of an occupant. A large number of sensors can be deployed and embedded in places or things. All sensor nodes measure the physical world and send data to the base station for processing. However, the processing of all collected data from every sensor node can consume significant energy and time. In order to enhance the sensor network in smart home applications, we propose the irrelevant data elimination based on k-means clustering algorithm to enhance data aggregation. This approach embeds the cluster head–based algorithm into cluster heads to omit irrelevant data from the base station. The pattern of measured data in each room can be clustered as an active pattern when human activity happens in that room and a stable pattern when human activity does not happen in the room. The irrelevant data elimination based on k-means clustering algorithm approach can reduce 55.94% of the original data with similar results in human activity classification. This study proves that the proposed approach can eliminate meaningless data and intelligently aggregate data by delivering only data from rooms in which human activity likely occurs.
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors

We study the linear subspace fitting problem in the overparameterized setting, where the estimated subspace can perfectly interpolate the training examples. Our scope includes the least-squares solutions to subspace fitting tasks with varying levels of supervision in the training data (i.e., the proportion of input-output examples of the desired low-dimensional mapping) and orthonormality of the vectors defining the learned operator. This flexible family of problems connects standard, unsupervised subspace fitting that enforces strict orthonormality with a corresponding regression task that is fully supervised and does not constrain the linear operator structure. This class of problems is defined over a supervision-orthonormality plane, where each coordinate induces a problem instance with a unique pair of supervision level and softness of orthonormality constraints. We explore this plane and show that the generalization errors of the corresponding subspace fitting problems follow double descent trends as the settings become more supervised and less orthonormally constrained.
Sensitivity-Based Change Detection for Dynamic Constrained Optimization

In dynamic constrained optimization, changes may occur in either the objective function or constraint functions, or both.However, although research on dynamic optimization has been growing significantly, it is centered mainly around unconstrained problems.On the other hand, research on dynamic constrained problems has considered simple extensions of those conducted on unconstrained ones.These approaches are usually computationally expensive and exhibit slow convergence in changed environments.To develop an effective algorithm for dynamic constrained problems with changes occurring in only the constraint's space, in this research, we propose a sensitive constraint detection mechanism that provides valuable information for determining the movements of solutions in changing environments.As, by incorporating it with a search process, the convergence of an algorithm can be accelerated, it is adopted with a multi-operator evolutionary type of algorithm which is then tested on a set of constrained dynamic problems.The experimental results clearly demonstrate the benefits of this proposed approach.
Incidental learning of a visuo-motor sequence modulates saccadic amplitude: Evidence from the serial reaction time task.

This study examined the changes in saccadic amplitude associated with learning a visual sequence. The oculomotor system gradually adjusts saccadic parameters when tracking a visual stimulus, which has a predictable trajectory. In these contexts, the change in saccadic amplitudes leads to predictive fixations. That is, fixations made to a position in the visual field, where a visual stimulus is anticipated to appear. This study tested whether learning a sequence of visual target movements on the serial reaction time (SRT) task would affect saccadic amplitudes and in turn lead to predictive fixations. Participants in this study were 40 healthy adults who completed the SRT task. Both manual reaction times (RTs) and eye movements were recorded. Analyses of manual RT data showed that at the group level, the sequence was learnt. Analyses of the oculomotor data revealed saccadic amplitudes decreased, following repeated exposures to the visual sequence. Also, increasingly more accurate predictive fixations were observed, following multiple exposures to the sequence. Additional analyses revealed sequence learning related changes in manual RTs, and saccadic amplitudes were not correlated. This study demonstrates the oculomotor system can acquire information about the sequence on the SRT task to update saccadic amplitudes. The nonsignificant correlation between manual RTs and saccadic amplitudes may be indicating sequences are represented differently by the manual and oculomotor systems. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Smoothing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> Regularization for Extreme Learning Machine

Extreme learning machine (ELM) has been put forward for single hidden layer feedforward networks. Because of its powerful modeling ability and it needs less human intervention, the ELM algorithm has been used widely in both regression and classification experiments. However, in order to achieve required accuracy, it needs many more hidden nodes than is typically needed by the conventional neural networks. This paper considers a new efficient learning algorithm for ELM with smoothing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> regularization. A novel algorithm updates weights in the direction along which the overall square error is reduced the most and then this new algorithm can sparse network structure very efficiently. The numerical experiments show that the ELM algorithm with smoothing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> regularization has less hidden nodes but better generalization performance than original ELM and ELM with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math> regularization algorithms.
“Wake Neutrality” of Artificial Intelligence Devices

This chapter introduces the notion of “wake neutrality” of artificial intelligence devices and reviews its implication for wake-word approaches in open conversational commerce (OCC) devices such as Amazon’s Alexa, Google Home and Apple’s Siri. Examples illustrate how neutrality requirements such as explainability, auditability, quality, configurability, institutionalization, and non-discrimination may impact the various layers of a complete artificial intelligence architecture stack. The legal programming implications of these requirements for algorithmic law enforcement are also analysed. The chapter concludes with a discussion of the possible role of standards bodies in setting a neutral, secure and open legal programming voice name system (VNS) for human-to-AI interactions to include an “emotional firewall.”
STUDENTS’ PERCEPTION OF USING PADLET IN LEARNING ENGLISH AT THE FIRST SEMESTER OF ISLAMIC EDUCATION DEPARTMENT STAI HUBBULWATHAN DURI

This study aimed to know students’ perception of using Padlet in learning English in the first semester of Islamic Education Department STAI Hubbulwathan Duri. This research used a quantitative approach. The design of the research was a survey. In this research, the writer conducted a descriptive survey. The total population was the first semester of Islamic education STAI Hubbulwathan Duri. The undergraduate students have the same qualification and academic background before doing a survey. They had ever learned English by using Padlet. The sample of this research was 61 respondents. The total rating percentage of students’ perception using Padlet in learning English 54.5%. it was the agreed category. This category showed the respondents Agree to use Padlet application in the teaching-learning process. Based on the overall mean is 3.15 and the standard deviation is 0.673 respondents agree used it in the teaching-learning process. The respondents gave positive responses. It meant that the Padlet platform gave a positive perception to use it in the English learning process.
FPGA Implementation of Image Cryptology using Reversible Logic Gates

Reversible calculations have lot of applications in various fields like in Nanotechnology, Low power CMOS, Optical computing, Digital Image Processing, etc. Reversible calculations are the best systematic ways to turn down heat dissipation than any other standard methods.The key requirement for reversibility is for each input and output vector to have a one-to-one relation and it is very necessary as there is no information loss in the reversible calculation which reduces the power dispersion.Ideally, zero energy is dissipated by reversible circuits.Cryptology is used to provide security and privacy to authenticated users.In this paper an Image Cryptology (IC) is proposed with secret key generated using Runtime Linear Feedback Shift Register Logic (RLFSRL) to encrypt and decrypt data using Reversible Logic Gates (RLG) on Field Programmable Gate Array (FPGA).ICRLG designed in verilog hardware description language (HDL) which is synthesized, simulated and implemented on Vivado and targeted on Artix-7 XC7A35T-1-CPG236 architecture.
Privacy as a Feature for Body-Worn Cameras [In the Spotlight]

In this article, we discuss the threat to privacy that this passive data collection creates, along with opportunities to mitigate this risk. Furthermore, we argue that the use case of BWCs at work will stimulate the development of solutions that prevent the collection of data that could infringe upon the privacy of the wearer. Finally, we discuss the desirable properties of privacy-enhancing technologies (PETs) for BWC
A Partial Store-and-Forward Scheduling Method for Inter-Datacenter Bulk Data Transfers

Temporarily storing delay-tolerant data at peak hours and forwarding the data at off-peak hours, i.e., performing Store-and-Forward (SnF) using datacenter storage, can mitigate peak-hour congestion and exploit off-peak-hour bandwidth in inter-datacenter networks. Most prior studies considered a case where all nodes along their routing paths provided SnF options for the scheduling decision making. Intuitively, their solutions maximize the scheduling flexibility. However, the computational complexity of their solutions increases exponentially with the hop count. Meanwhile, SnF operations are generally performed on a portion of nodes rather than every node along the path. Thus, the considered case seems to be unnecessary in practice. In this paper, our studies reveal that desirable performance can be attained by involving a portion of nodes rather than all nodes along the path in the decision making. Thus, we propose a partial SnF scheduling method, which involves a portion of nodes in scheduling and introduces a network abstraction based on the involved nodes. Simulations demonstrate that the proposed method has lower complexity while achieving high performance.
Natural Image Deblurring Based on Ringing Artifacts Removal via Knowledge-Driven Gradient Distribution Priors

Blind image deblurring, composed of estimating blur kernel and non-blind deconvolution, is an extremely ill-posed problem. However, previous deblurring methods still cannot solve delta kernel or noise problem well and avoid ringing artifacts in restored image, because the reliable kernel estimation and image restoration could not be given from information-deficiency input without using natural image priors. In this work, we find that the blur process changes the distribution of the image gradient, and thus attempt to use the priori knowledge for guiding blind deblurring. For the sake of convenience of modeling, we come up with a simplified approximate formulation of the image gradient distribution prior, thus the restoration model using it can be solved by the method of iteratively reweighted least squares (IRLS). We also concentrate on how to optimize the models and develop an algorithm based on gradient prior and image structure: first, we computed image structure based on the total variation model and adaptive weight strategy and then estimated the strong edges from it. Those strong edges (structure) that have a possible adverse effect on blur kernel estimation can be removed. Next, we followed an alternate iterative framework to obtain high-quality blur kernel estimation by estimating blur kernel from strong structure and then restoring a latent image guided by the formulated clear image priors. Finally, we proposed a non-blind deconvolution method based on fitted multi-order gradient priors as regularization to restore the latent image. In addition, we analyze the effectiveness of the knowledge-driven prior in image deblurring and demonstrate that it can favor clear images over blurred ones and restrain ringing artifacts effectively. Extensive experiments verify the superiority of the proposed method over state-of-the-art algorithms compared, both qualitatively and quantitatively.
Fuzzy C Means Imputation of Missing Values with Ant Colony Optimization

Missing value is an error that always happened, and it is unavoidable.This error should be handled correctly before data is processed into processing model.This paper proposes a improved method of imputation by employing a new version of Fuzzy c Means (FCM) which hybridized with Evolutionary Algorithm to handle missing values problem.Missing values can be treated by imputing the values.The advantage of FCM is it can provide a better separation of instances where it is not well separated.It is a well-known classification method that can provide highest accuracy.It can be benefit from Ant Colony Optimization that can help to select only highly related feature to be process as an estimation for a missing value.Here, a traditional FCM basic is test as a cluster technique for imputed data.
Evolutionary Automatic Text Summarization using Cluster Validation Indexes

The main problem for generating an extractive automatic text summary (EATS) is to detect the key themes of a text. For this task, unsupervised approaches cluster the sentences of the original text to find the key sentences that take part in an automatic summary. The quality of an automatic summary is evaluate dusing similarity metrics with human-made summaries. However, the relationship between the quality of the human-made summaries and the internal quality of the clustering is unclear. First, this paper proposes a comparison of the correlation of the quality of a human-made summary to the internal quality of the clustering validation index for finding the best correlation with a clustering validation index. Second, in this paper, an evolutionary method based on the best above internal clustering validation index for an automatic text summarization task is proposed. Our proposed unsupervised method for EATS has the advantage of not requiring information regarding the specific classes or themes of a text, and is therefore domain and language independent. The high results obtained by our method, using the most-competitive standard collection for EATS, prove that our method maintains a high correlation with human-made summaries, meeting the specific features of the groups, for example, compaction, separation, distribution, and density.
Directional lifting wavelet transform based SAR Image Compression

Data compression technology is an effective way to alleviate the pressure of transmission and storage on SAR system.The complex SAR image consists of amplitude and phase, the first-level image data of the SAR system.The information fidelity in SAR image is crucial to some special applications, such as interferometers and moving target detection.The objective of this scheme is to assess the potential of the Contourlet Transform (CT) in relation to the issue of noise removal in SAR intensity images.The contourlet transform can be seen as a filter bank implementation of the curvelet transform.This novel approach to non linear approximation aims at providing a better representation of the geometrical content of natural images.Recently, a stationary version has been proposed that preserves translation invariance.We compare the CT performances against the curvelet transform and the stationary wavelet transform for two different noise reduction techniques.Results indicate a better compromise between noise removals and also the proposed scheme significantly outperforms DLWT based scheme in terms of higher PSNR and compression ratio with better representation of SAR image.