Reviewerly/exHarmony huggingface dataset:
Forensic analysis for AI speaker with display Echo Show <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mrow><mml:mi>n</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> generation as a case study

As the Internet of Things (IoT) era arrives, many Internet-connected devices are being released, and their use is increasing. One of these, the AI speaker, is designed to augment user convenience by using voice recognition. The best-known products are the Amazon Echo family, including Echo and Echo Dot and more recently, Echo Show with display. An AI speaker with display provides diverse functions such as surfing the Internet, taking pictures, making voice or video calls, and controlling smart home devices. To do this, Alexa cloud servers store a variety of configuration values and historical logs, and users can manage their own cloud-native data through interfaces (e.g., Web sites or mobile apps). For this reason, AI speakers with smart display are similar to PCs or smartphones, which can be very profitable from a digital forensic perspective. This paper focuses on detailed research on the second generation of Echo Show. The first step was to collect forensic artifacts stored inside the product by teardown, identifying eMMC flash memory chips and performing chip-off on Echo Show. Alexa app-related artifacts used on smartphones and how to automatically acquire data from the Alexa cloud were also investigated. From three sources including Echo Show, a companion client (smartphone), and the Alexa cloud, it was possible to acquire user credentials, traces of photos, records of watching videos, log files, and Internet histories with timestamp. The second step was to identify the possibility of inferring new information by correlating artifacts collected from different sources. Integrative analysis enables investigators to track suspect activity across digital devices. Third, this paper introduces an updated version of Cloud-based IoT Forensic Toolkit (CIFT) to support digital investigation of Echo Show. Based on the technical findings, this study proposes a digital forensic framework for a smart speaker with a display that can play an important role as a digital witness at a crime scene. Until now, there has been no multilevel approach to acquisition and analysis of Echo Show data in the field of digital forensics. Therefore, this study makes a contribution to the digital forensic community.
Social network-based News Recommendation with Knowledge Graph

News recommendations aimed at alleviating network information overload. Traditional methods cannot simultaneously consider the representation of entities in the news at the level of knowledge and the influence of social networks on user interest points. The above two factors have a huge correlation with the efficiency of news recommendation. In order to consider these factors, this paper proposes a news recommendation model based on knowledge graph and social network which integrates knowledge graph representation and social networks into news recommendations. The model is a deep recommendation framework based on content and social networks for click-through rate prediction. The model utilizes the knowledge graph for representing entities in the news, quantifies the impact of social networks, and capture the dynamic changes of user interest. It adopts an improved sampling mechanism to quantify the social network structure. It uses a random walk sampling strategy to obtain neighbors in the social network. Moreover, it obtains the neighbor's influence weight on the target from interaction and content. The attention mechanism is used to quantify the effects of browsing records on user interests to capture dynamic changes. Experiments show that our model can effectively improve the effectiveness of news recommendations.
Anticipatory Classifier System with Average Reward Criterion in Discretized Multi-Step Environments

Initially, Anticipatory Classifier Systems (ACS) were designed to address both single and multistep decision problems. In the latter case, the objective was to maximize the total discounted rewards, usually based on Q-learning algorithms. Studies on other Learning Classifier Systems (LCS) revealed many real-world sequential decision problems where the preferred objective is the maximization of the average of successive rewards. This paper proposes a relevant modification toward the learning component, allowing us to address such problems. The modified system is called AACS2 (Averaged ACS2) and is tested on three multistep benchmark problems.
Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification

Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.
A Preliminary Investigation into a Deep Learning Implementation for Hand Tracking on Mobile Devices

Hand tracking is an essential component of computer graphics and human-computer interaction applications. The use of RGB camera without specific hardware and sensors (e.g., depth cameras) allows developing solutions for a plethora of devices and platforms. Although various methods were proposed, hand tracking from a single RGB camera is still a challenging research area due to occlusions, complex backgrounds, and various hand poses and gestures. We present a mobile application for 2D hand tracking from RGB images captured by the smartphone camera. The images are processed by a deep neural network, modified specifically to tackle this task and run on mobile devices, looking for a compromise between performance and computational time. Network output is used to show a 2D skeleton on the user's hand. We tested our system on several scenarios, showing an interactive hand tracking level and achieving promising results in the case of variable brightness and backgrounds and small occlusions.
Exposing Speech Transsplicing Forgery with Noise Level Inconsistency

Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.
Probing the Mystery of Cryptocurrency Theft: An Investigation into Methods for Cryptocurrency Tainting Analysis

Since the creation of Bitcoin, transaction tracking is one of the prominent means for following the movement of Bitcoins involved in illegal activities. Although every Bitcoin transaction is recorded in the blockchain ledger, which is transparent for anyone to observe and analyse, Bitcoin's pseudonymity system and transaction obscuring techniques still allow criminals to disguise their transaction trail. While there have been a few attempts to develop tracking methods, there is no accepted evaluation method to measure their accuracy. Therefore, this paper introduces a metrics-based evaluation framework to investigate the performance indicators of a theft transaction tracking. We use this framework to investigate strategies for transaction tracking. This paper also introduces two new tainting methods and an address profiling approach. We run an experiment to compare the accuracy of tainting strategies using data from real Bitcoin theft and a set of controls.
MAC Aware Quantization for Distributed Gradient Descent

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL over a MAC. We focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (e.g., rate or bandwidth) to different users based on a) the informativeness of the gradients at users, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.
Index selection for NoSQL database with deep reinforcement learning

With the development of big data technology, the data management of complex applications has become more and more resource intensive. In this paper, we propose an automatic approach (DRLISA) to achieve NoSQL database index selection. For different workloads, we automatically select its corresponding indexes and parameters which can totally improve the database performance. Our DRLISA establishes an optimal index by building a deep reinforcement learning model which is able to adapt the dynamic change of workloads. We conducted our experiments in five aspects (the impact of data manipulation, the impact of operation count, comparison with random selection, comparison with existing method and the robustness of DRLISA) using the open source benchmark, YCSB. The experimental results showed that DRLISA has a high efficient index recommendation under the dynamic workloads.
Hyperbolic User Identity Linkage across Social Networks

With the growing prosperity and diversity of social networks, more and more users participate in multiple social networks to enjoy their diverse services. Users can create different user identities in different social networks, but most networks are independent of each other. Hence, a fundamental question arises: can we align user identities across social networks? Related work mostly focuses on Euclidean geometry to learn representation vectors of social networks. However, motivated by recent advances in geometry representation learning, we find that hyperbolic geometry shows the advantage of expressing network hierarchical structure, while Euclidean geometry doesn't. Thus, in this paper, we first introduce the connection between hyperbolic space and social networks. Then we propose a novel hyperbolic geometry representation learning model for user identity linkage across social networks, which is called “HUIL”. Finally, we conduct comprehensive experiments on real-world datasets and verify the superiority of HUIL for user identity linkage.
Strategic argumentation dialogues for persuasion: Framework and experiments based on modelling the beliefs and concerns of the persuadee

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is “good” in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants that compares an automated persuasion system based on this technology with a baseline system that does not take the beliefs and concerns into account in its strategy.
Alzheimer’s Disease Stage Classification using Deep Convolutional Neural Networks on Oversampled Imbalance Data

One problem that is often faced in health data sets is a high level of imbalance. Imbalanced data can mean that the data used in machine learning has an unbalanced data distribution between different classes. One of the data is Alzheimer's disease data. The data used are Dataset containing 6400 images of brain magnetic resonance imaging that is indicated as having Alzheimer's disease or not. This study used Deep Learning Architecture to classify brains affected by Alzheimer's disease and healthy brains. We produced a trained and predictive model using Deep Convolutional Neural Networks (CNN), where the data had previously gone through the oversampling stage due to an imbalance in the data distribution. From the evaluation metric table provided in this study, the highest accuracy for detecting Alzheimer's disease was obtained by ResNetl8 with an accuracy of 60.67%. Still, the shortest training time was obtained by ShuffleNet V2 with 624.4 seconds using non-oversampled. While the results obtained by using oversampled, ShuffleNet V2 provide 60.75% accuracy with the fastest training time of 1478.6 seconds. The special CNN model that the author proposes gets an accuracy of 50% using non-oversampled and 55.27% accuracy using oversampled.
S2CFT: A New Approach for Paper Submission Recommendation

There have been a massive number of conferences and journals in computer science that create a lot of difficulties for scientists, especially for early-stage researchers, to find the most suitable venue for their scientific submission. In this paper, we present a novel approach for building a paper submission recommendation system by using two different types of embedding methods, GloVe and FastText, as well as Convolutional Neural Network (CNN) and LSTM to extract useful features for a paper submission recommendation model. We consider seven combinations of initial attributes from a given submission: title, abstract, keywords, title + keyword, title + abstract, keyword + abstract, and title + keyword + abstract. We measure these approaches' performance on one dataset, presented by Wang et al., in terms of top K accuracy and compare our methods with the S2RSCS model, the state-of-the-art algorithm on this dataset. The experimental results show that CNN + FastText can outperform other approaches (CNN + GloVe, LSTM + GloVe, LSTM + FastText, S2RSCS) in term of top 1 accuracy for seven types of input data. Without using a list of keywords in the input data, CNN + GloVe/FastText can surpass other techniques. It has a bit lower performance than S2RSCS in terms of the top 3 and top 5 accuracies when using the keyword information. Finally, the combination of S2RSCS and CNN + FastText, namely S2CFT, can create a better model that bypasses all other methods by top K accuracy (K = 1,3,5,10).
Characterizing noise correlation and enhancing coherence via qubit motion

The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices, but it has barely been studied so far. In this work, we utilize an effective method called qubit motion, to efficiently determine the noise correlations between any pair of qubits in a 7-qubit superconducting quantum system. The noise correlations between the same pairs of qubits are also investigated when the qubits are at distinct operating frequencies. What’s more, in this multi-qubit system with the presence of noise correlations, we demonstrate the enhancing effect of qubit motion on the coherence of logic qubits, and we propose a Motion-CPMG operation sequence to more efficiently protect the logic state from decoherence, which is experimentally demonstrated to extend the coherence time of logic qubits by nearly one order of magnitude.
Energy Efficient Data Centers Powered by On-site Renewable Energy and UPS Devices

To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability over traditional centralized UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewable-energy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.
Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

The heterogeneity of today’s Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user’s information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. By embedding a query and the data in the same vector space, the relations across modalities are made explicit and exploited for more accurate query evaluation. At the same time, multi-modal queries are answered with a single pass over the data. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.
Personogenesis Through Imitating Human Behavior in a Humanoid Robot “Alter3”

In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a person in front of it and stores the motion sequences in its memory. Alter3 also uses a self-simulator to simulate its own motions before executing them and generates a self-image. If the visual perception (of a person's motion being imitated) and the imitating self-image differ significantly, Alter3 retrieves a motion sequence closer to the target motion from its memory and executes it. We investigate how this mimetic behavior develops interacting with human, by analyzing memory dynamics and information flow between Alter3 and a interacting person. One important observation from this study is that when Alter3 fails to imitate a person's motion, the person tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. This spontaneous role-switching behavior between a human and Alter3 is a way to initiate personality formation (i.e., personogenesis) in Alter3.
Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF

Recently, Indonesian sentiment analysis is a hot research field, especially for twitter data. Many approaches can be used to classify people's sentiment on Twitter. One of the best methods in sentiment analysis is machine learning. SVM is a machine learning algorithm that has good performance. However, some of the findings of SVM research have different results, which are no better than other algorithms. It is because each study uses a different SVM configuration. Kernel functions on SVM play an essential role in improving SVM performance. Choosing the sui table Kernel function is also crucial for every particular application of SVM. Therefore, to address the problem, we compared four kernel functions on SVM with TF-IDF, such as Polynomial, Sigmoid, Linear, and Radial Basis Function (RBF) kernel. TF-IDF is employed as a feature extraction and selection to improve SVM performance. We used 4000 tweets about omnibus law issues in Indonesia, and 10-fold cross-validation and confusion matrix were employed to validate and evaluate our models. Based on the experimental results, the RBF kernel function on SVM+TF-IDF using 2000 features achieved the best performance of all feature tests in accuracy, precision, recall, and f-measure with value of 96.61%, 96.70%, 96.58%, 96.60%, respectively. Besides, the RBF kernel function obtained high performance when using low dimensionality of the feature. Meanwhile, the Linear kernel function reached the best performance when using high dimensionality of the feature.