Reviewerly/exHarmony huggingface dataset:
Automated Decision-based Adversarial Attacks

Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples. In this work, we consider the practical and challenging decision-based black-box adversarial setting, where the attacker can only acquire the final classification labels by querying the target model without access to the model's details. Under this setting, existing works often rely on heuristics and exhibit unsatisfactory performance. To better understand the rationality of these heuristics and the limitations of existing methods, we propose to automatically discover decision-based adversarial attack algorithms. In our approach, we construct a search space using basic mathematical operations as building blocks and develop a random search algorithm to efficiently explore this space by incorporating several pruning techniques and intuitive priors inspired by program synthesis works. Although we use a small and fast model to efficiently evaluate attack algorithms during the search, extensive experiments demonstrate that the discovered algorithms are simple yet query-efficient when transferred to larger normal and defensive models on the CIFAR-10 and ImageNet datasets. They achieve comparable or better performance than the state-of-the-art decision-based attack methods consistently.
Unsupervised Remote Sensing Super-Resolution via Migration Image Prior

Recently, satellites with high temporal resolution have fostered wide attention in various practical applications. Due to limitations of bandwidth and hardware cost, however, the spatial resolution of such satellites is considerably low, largely limiting their potentials in scenarios that require spatially explicit information. To improve image resolution, numerous approaches based on training low-high resolution pairs have been proposed to address the super-resolution (SR) task. Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use. In this paper, we proposed a new unsupervised learning framework, called "MIP", which achieves SR tasks without low/high resolution image pairs. First, random noise maps are fed into a designed generative adversarial network (GAN) for reconstruction. Then, the proposed method converts the reference image to latent space as the migration image prior. Finally, we update the input noise via an implicit method, and further transfer the texture and structured information from the reference image. Extensive experimental results on the Draper dataset show that MIP achieves significant improvements over state-of-the-art methods both quantitatively and qualitatively. The proposed MIP is open-sourced at http://github.com/jiaming-wang/MIP.
Context-Based Soft Actor Critic for Environments with Non-stationary  Dynamics

The performance of deep reinforcement learning methods prone to degenerate when applied to environments with non-stationary dynamics. In this paper, we utilize the latent context recurrent encoders motivated by recent Meta-RL materials, and propose the Latent Context-based Soft Actor Critic (LC-SAC) method to address aforementioned issues. By minimizing the contrastive prediction loss function, the learned context variables capture the information of the environment dynamics and the recent behavior of the agent. Then combined with the soft policy iteration paradigm, the LC-SAC method alternates between soft policy evaluation and soft policy improvement until it converges to the optimal policy. Experimental results show that the performance of LC-SAC is significantly better than the SAC algorithm on the MetaWorld ML1 tasks whose dynamics changes drasticly among different episodes, and is comparable to SAC on the continuous control benchmark task MuJoCo whose dynamics changes slowly or doesn't change between different episodes. In addition, we also conduct relevant experiments to determine the impact of different hyperparameter settings on the performance of the LC-SAC algorithm and give the reasonable suggestions of hyperparameter setting.
Black-box equivalence checking of quantum circuits by nonlocality

Suppose two quantum circuit chips are located at different places, for which we do not have any prior knowledge, and cannot see the internal structures either. If we want to find out whether they have the same functions or not with certainty, what should we do? In this paper, we show that this realistic problem can be solved from the viewpoints of quantum nonlocality. Specifically, we design an elegant protocol that examines underlying quantum nonlocality. We prove that in the protocol the strongest nonlocality can be observed if and only if two quantum circuits are equivalent to each other. We show that the protocol also works approximately, where the distance between two quantum circuits can be lower and upper bounded analytically by observed quantum nonlocality. Furthermore, we also discuss the possibility to generalize the protocol to multipartite cases, i.e., if we do equivalence checking for multiple quantum circuits, we try to solve the problem in one go. Our work introduces a nontrivial application of quantum nonlocality in quantum engineering.
Schematic Memory Persistence and Transience for Efficient and Robust  Continual Learning

Continual learning is considered a promising step towards next-generation Artificial Intelligence (AI), where deep neural networks (DNNs) make decisions by continuously learning a sequence of different tasks akin to human learning processes. It is still quite primitive, with existing works focusing primarily on avoiding (catastrophic) forgetting. However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of 1) memory efficiency, 2) generalizability, and 3) robustness when dealing with noisy data. To address this, we propose a novel ScheMAtic memory peRsistence and Transience (SMART) framework for continual learning with external memory that builds on recent advances in neuroscience. The efficiency and generalizability are enhanced by a novel long-term forgetting mechanism and schematic memory, using sparsity and 'backward positive transfer' constraints with theoretical guarantees on the error bound. Robust enhancement is achieved using a novel short-term forgetting mechanism inspired by background information-gated learning. Finally, an extensive experimental analysis on both benchmark and real-world datasets demonstrates the effectiveness and efficiency of our model.
Canonical Saliency Maps: Decoding Deep Face Models

As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has seen an upswing, where any failure may have far-reaching consequences. We need methods to build trust in deployed systems by making their working as transparent as possible. Existing visualization algorithms are designed for object recognition and do not give insightful results when applied to the face domain. In this work, we present 'Canonical Saliency Maps', a new method that highlights relevant facial areas by projecting saliency maps onto a canonical face model. We present two kinds of Canonical Saliency Maps: image-level maps and model-level maps. Image-level maps highlight facial features responsible for the decision made by a deep face model on a given image, thus helping to understand how a DNN made a prediction on the image. Model-level maps provide an understanding of what the entire DNN model focuses on in each task and thus can be used to detect biases in the model. Our qualitative and quantitative results show the usefulness of the proposed canonical saliency maps, which can be used on any deep face model regardless of the architecture.
A Study into patient similarity through representation learning from  medical records

Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines.
Phrase break prediction with bidirectional encoder representations in  Japanese text-to-speech synthesis

We propose a novel phrase break prediction method that combines implicit features extracted from a pre-trained large language model, a.k.a BERT, and explicit features extracted from BiLSTM with linguistic features. In conventional BiLSTM based methods, word representations and/or sentence representations are used as independent components. The proposed method takes account of both representations to extract the latent semantics, which cannot be captured by previous methods. The objective evaluation results show that the proposed method obtains an absolute improvement of 3.2 points for the F1 score compared with BiLSTM-based conventional methods using linguistic features. Moreover, the perceptual listening test results verify that a TTS system that applied our proposed method achieved a mean opinion score of 4.39 in prosody naturalness, which is highly competitive with the score of 4.37 for synthesized speech with ground-truth phrase breaks.
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation  Pairs

Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on eight multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.
Iterative Alignment Flows

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair representations, batch effect mitigation, and unsupervised domain adaptation. Existing flow-based approaches estimate multiple flows independently, which is equivalent to learning multiple full generative models. Other approaches require adversarial learning, which can be computationally expensive and challenging to optimize. Thus, we aim to jointly align multiple distributions while avoiding adversarial learning. Inspired by efficient alignment algorithms from optimal transport (OT) theory for univariate distributions, we develop a simple iterative method to build deep and expressive flows. Our method decouples each iteration into two subproblems: 1) form a variational approximation of a distribution divergence and 2) minimize this variational approximation via closed-form invertible alignment maps based on known OT results. Our empirical results give evidence that this iterative algorithm achieves competitive distribution alignment at low computational cost while being able to naturally handle more than two distributions.
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent  Reinforcement Learning

Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks. Two main promising research directions are multi-agent value function decomposition and multi-agent policy gradients. In this paper, we propose a new decomposed multi-agent soft actor-critic (mSAC) method, which effectively combines the advantages of the aforementioned two methods. The main modules include decomposed Q network architecture, discrete probabilistic policy and counterfactual advantage function (optinal). Theoretically, mSAC supports efficient off-policy learning and addresses credit assignment problem partially in both discrete and continuous action spaces. Tested on StarCraft II micromanagement cooperative multiagent benchmark, we empirically investigate the performance of mSAC against its variants and analyze the effects of the different components. Experimental results demonstrate that mSAC significantly outperforms policy-based approach COMA, and achieves competitive results with SOTA value-based approach Qmix on most tasks in terms of asymptotic perfomance metric. In addition, mSAC achieves pretty good results on large action space tasks, such as 2c_vs_64zg and MMM2.
5G Cellular Network Forensics

The fifth generation (5G) of cellular networks will bring 10Gbps user speeds, have a wider bandwidth which can accommodate 1000 times what the fourth generation (4G) could and 100 times faster than the previous 4G cellular network. This paper’s focus is to highlight on the legal aspects of the 5G cellular network. That is, the health issues, privacy, and security issues on Africans. Most crimes currently are facilitated by cellular network devices and with the improvement that the 5G cellular network brings, there is going to be an increase in cybercrime and hence the need for forensics. These forensics’ techniques used in evidence acquisition violates the privacy and security of the users of the 5G cellular network. Keywords: Cellular Networks, Fifth Generation (5G), Lawful Interception (LI), Lawful Access Location Services (LALS), Privacy, Security, Africa, Natural Resources, Carbon, Ozone Layer, Internet of Things (IoT) BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Edwin Angmor Kofi Ohipeni (2022): 5G Cellular Network Forensics Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 181-184 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P29
Phase-Oriented Advice and Review Structure (PARS) for Digital Forensic Investigations

The PARS is the first documented peer review methodology for the digital forensics field, a six staged approach designed to formally support organizations and their staff in their goal of facilitating effective peer review of digital forensic work, from investigative tasks to forensic activities and forensic analysis processes (Pollitt et al., 2018). This assignment discusses how the PARS methodology can be implemented, and the available options and mechanisms available to ease the interpretation of this model into existing practices. Both the early ‘Advisor’ and later ‘Reviewer’ roles in PARS are discussed and their requirements and expectations are defined. Keywords: Digital forensics, Peer review, Digital evidence, Quality assurance, Forensic science Multi-staged Rview, Multi-person Review BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Aliloulaye Tchaou (2022): Phase-Oriented Advice and Review Structure (PARS) for Digital Forensic Investigations Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 173-180 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P28
A Data-Driven Mobile Community Application for Book Recommendation and Personalization using AI and Machine Learning

Knowing a movie or a book fits your flavor without finishing the whole film or the book? Although there are many ways to find a summary of a film or a book, having an app that generates needed information according to the genre will make things much more manageable. This paper develops a mobile app named Book and Movie Search that uses API or the online database to generalize data such as authors, plots, overview, and more with a few clicks. The results show that within seconds, a list of information will show according to movies and books, and a qualified way to find information using the Book And Movie Search app. For example, if one decided to buy a book named Flipped and did not have time to finish the whole book, he can enter the name on the app. It will generate a book summary that quickly gives him more information about it and help him decide whether he wishes to make the purchase.
Survey of Secure Network Protocols: United States Related Domains

Over time, the HTTP Protocol has undergone significant evolution. HTTP was the internet's foundation for data communication. When network security threats became prevalent, HTTPS became a widely accepted technology for assisting in a domain’s defense. HTTPS supported two security protocols: secure socket layer (SSL) and transport layer security (TLS). Additionally, the HTTP Strict Transport Security (HSTS) protocol was included to strengthen the HTTPS protocol. Numerous cyber-attacks occurred in the United States, and many of these attacks could have been avoided simply by implementing domains with the most up-to-date HTTP security mechanisms. This study seeks to accomplish two objectives: 1. Determine the degree to which US-related domains are configured optimally for HTTP security protocol setup; 2. Create a generic scoring system for a domain's network security based on the following factors: SSL version, TLS version, and presence of HSTS to easily determine where a domain stands. We found through our analysis and scoring system incorporation that US-related domains showed a positive trend for secure network protocol setup, but there is still room for improvement. In order to safeguard unwanted cyber-attacks, current HTTP domains need to be extensively investigated to identify if they possess security-related components. Due to the infrequent occurrence of HSTS in the evaluated domains, the computer science community necessitates further HSTS education.
Mistakes of a Popular Protocol Calculating Private Set Intersection and Union Cardinality and its Corrections

In 2012, De Cristofaro et al. proposed a protocol to calculate the Private Set Intersection and Union cardinality (PSI-CA and PSU-CA). This protocol's security is based on the famous DDH assumption. Since its publication, it has gained lots of popularity because of its efficiency (linear complexity in computation and communication) and concision. So far, it's still considered one of the most efficient PSI-CA protocols and the most cited (more than 170 citations) PSI-CA paper based on the Google Scholar search. However, when we tried to implement this protocol, we couldn't get the correct result of the test data. Since the original paper lacks of experimental results to verify the protocol's correctness, we looked deeper into the protocol and found out it made a fundamental mistake. Needless to say, its correctness analysis and security proof are also wrong. In this paper, we will point out this PSI-CA protocol's mistakes, and provide the correct version of this protocol as well as the PSI protocol developed from this protocol. We also present a new security proof and some experimental results of the corrected protocol.
An Intelligent Video Editing Automate Framework using AI and Computer Vision

At present, many video editing software have been created, but what they all have in common is that they require manual work to edit. And it takes a lot of time and the user needs to watch each frame before editing. In this paper, we have developed a program about AI intelligence. The most important point of this software is that it can automatically focus the face of a person and edit only selected clips of the person to make a complete video. Users only need to prepare the video they want to edit and a photo of the main character. Then, upload both to the software and AI Intelligence will automatically edit it, providing the user with a way to download and save it after editing the main character they need. we applied our application to an example video using the Marvel character Hawkeye as he appears in End Game, and tried many experiments with the clip, eventually we tried many experiments with the clip and finally got a video of our selected character. The results show that this software saves the user a lot of time and is highly efficient. All operations are carried out by AI.
Meningkatkan Motivasi Belajar Bahasa Inggris Middle Childhood di Panti Asuhan Sinar Damai Sejahtera Menjadi Lebih Baik

English As a foreign language, our ability to master English is very important in this four point zero era because English is not only used in education, but also in the world of work. However, we know that many Indonesians have low English skills, especially those from underprivileged groups such as orphans, abandoned children, children with disabilities (ADK). Many of the children at the Sinar Damai Sejahtera Orphanage are interested in learning English, but the unfavorable conditions for them, the very expensive English course, becomes an obstacle for them. Therefore, the Field Work Practice which has been carried out for 3 months is very useful for them to be able to learn English without having to pay any fees. The program intervention used in this PKL activity is the Community Intervention proposed by Zastrow which consists of: Intake, Assessment and Intervention Planning, Member Selection, Group Development, Evaluation and Termination. There has been an increase in reading and speaking skills in English with many positive impacts after this program.