Reviewerly/exHarmony huggingface dataset:
Learning with Selective Forgetting

Lifelong learning aims to train a highly expressive model for a new task while retaining all knowledge for previous tasks. However, many practical scenarios do not always require the system to remember all of the past knowledge. Instead, ethical considerations call for selective and proactive forgetting of undesirable knowledge in order to prevent privacy issues and data leakage. In this paper, we propose a new framework for lifelong learning, called Learning with Selective Forgetting, which is to update a model for the new task with forgetting only the selected classes of the previous tasks while maintaining the rest. The key is to introduce a class-specific synthetic signal called mnemonic code. The codes are "watermarked" on all the training samples of the corresponding classes when the model is updated for a new task. This enables us to forget arbitrary classes later by only using the mnemonic codes without using the original data. Experiments on common benchmark datasets demonstrate the remarkable superiority of the proposed method over several existing methods.
Finding Open Content in the Library Is Surprisingly Hard

While library discovery systems offer great promise for delivering open access materials, they often fall short. Discovery tools merge multiple records together, and in the process show a preference for linking options that are less likely to successfully deliver open access full text. Third party tools offer options for bringing users to missing content, but these tools are often not fully supported in library systems. Library systems are so focused on keeping unauthorized users out of restricted content that it compromises their ability to get users into content that is freely available.
Quality Evaluation of Structural Design in Software Reverse Engineering: A Focus On Cohesion

Software reverse engineering (SRE) plays a crucial role in contemporary software environments. Software developers may implement a system first then use SRE tools to generate design content such as the Unified Modeling Language (UML) diagrams. In the literature of SRE, studies majorly focus on how precisely the conversion can reflect the system; there is, however, little or no research that further looks into the quality of the converted results. Therefore, this paper presents an online knowledge-based ontological SRE system, OntRECoh, for quality evaluation of converted UML structural models. OntRECoh features a domain-specific knowledge base that focuses on cohesion design and a rule-based inference engine for computing the cohesion scores of Java-based implemented systems and providing improvement recommendations through its Web-based interface. Furthermore, OntRECoh includes both static and dynamic cohesion measures from both the design and the implementation aspects, for the evaluation to be more comprehensive and synthetic in the SRE context.
Generalization of the Menegozzi and Lamb maser algorithm to the transient superradiance regime

ABSTRACT We investigate the application of the conventional quasi-steady state maser modelling algorithm of Menegozzi &amp; Lamb (ML) to the high field transient regime of the one-dimensional Maxwell-Bloch (MB) equations for a velocity distribution of atoms or molecules. We quantify the performance of a first order perturbation approximation available within the ML framework when modelling regions of increasing electric field strength, and we show that the ML algorithm is unable to accurately describe the key transient features of R. H. Dicke’s superradiance (SR). We extend the existing approximation to one of variable fidelity, and we derive a generalization of the ML algorithm convergent in the transient SR regime by performing an integration on the MB equations prior to their Fourier representation. We obtain a manifestly unique integral Fourier representation of the MB equations which is $\mathcal {O}\left(N\right)$ complex in the number of velocity channels N and which is capable of simulating transient SR processes at varying degrees of fidelity. As a proof of operation, we demonstrate our algorithm’s accuracy against reference time domain simulations of the MB equations for transient SR responses to the sudden inversion of a sample possessing a velocity distribution of moderate width. We investigate the performance of our algorithm at varying degrees of approximation fidelity, and we prescribe fidelity requirements for future work simulating SR processes across wider velocity distributions.
Rogue Agent Identification and Collision Avoidance in Formation Flights using Potential Fields

In this paper, an artificial potential field based technique is proposed for maintaining a formation of drones, for identifying rogue drones, and for avoiding collision with obstacles and rogue drones. Rogue drone identification is done by using a threshold potential value which is also used to identify the threat level of neighbouring drones. A novel assistive potential field concept is introduced to implement a collision avoidance strategy that helps the formation to avoid collision with the rogue drone as well as other obstacles. A pair of hyperplanes are defined using which the repulsive forces are generated. Simulations are presented to demonstrate the efficacy of the approach.
Current Research Areas in Blockchain

A blockchain is a digital record of transactions.The name comes from its structure, in which individual records, called blocks, are linked together in single list, called a chain.It stands out with the rise of Bitcoin.Its popularity is increasing day by day.It provides anonymity, privacy and data integrity without any control organization.In this report, we will reveal the areas of research that have emerged.We will touch on what these research areas can bring forward.Articles are generally related to Bitcoin.But lately, this technology has entered many areas of our lives.Recommendations on future research directions are provided in this paper.
Russian Flagship Universities’ Incomes: Dynamics and Tendencies

This research article aims at identifying the impact of establishing flagship universities on the dynamics and structure of their income. The study is based on analyzing the growth rate of total income and income per 1 scientific and pedagogical worker in 2019–2020 as compared to 2015 reference year (taking inflation into account), together with finding out changes in the share of off-budget revenues, revenues from research and development work. There are used standard techniques of descriptive statistics, paired Student’s t -test for related samples, Mann-Whitney U -test, and cluster analysis. The total income of all flagship universities has grown, but, however, show deeper differentiation. The group of leaders have increased their revenues by 0,5–1,0 billion rubles in 2015 prices, but a number of universities face stagnated or declining income. The COVID-19 pandemic has had little or no impact on income differentiation and dyna mics. The by-income stratification per 1 scientific and pedagogical worker is lower, which indicates a personnel decrease in a number of clusters with stable or declining income. Neither institution’s joining the first or the second wave of flagship universities nor the availability of special federal funding has a statistically significant relationship with income dynamics. At the same time, the majority of flagship universities get the same stable share of income from research activities and did not decrease their dependence on the budget system. This indicates either the absence of major chan gesor the simultaneous growth of income from research activities and additional funding like federal programs, projects, grants. Not all flagship universities have been able to increase revenues, so the leaders’ experience requires further analysis. The results of the study may possibly be of interest for managers of both flagship and other categories of universities, as well as for those who are going to substantiate management decisions within the latest Priority-2030 program.
Vau Da Muntanialas: Energy-Efficient Multi-Die Scalable Acceleration of RNN Inference

Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn temporal dependencies by keeping an internal state, making them ideal for time-series problems such as speech recognition.However, the outputto-input feedback creates distinctive memory bandwidth and scalability challenges in designing accelerators for RNNs.We present MUNTANIALA, an RNN accelerator architecture for LSTM inference with a silicon-measured energy-efficiency of 3.25 TOP/s/W and performance of 30.53 GOP/s in UMC 65nm technology.The scalable design of MUNTANIALA allows running large RNN models by combining multiple tiles in a systolic array.We keep all parameters stationary on every die in the array, drastically reducing the I/O communication to only loading new features and sharing partial results with other dies.For quantifying the overall system power, including I/O power, we built VAU DA MUNTANIALAS, to the best of our knowledge, the first demonstration of a systolic multi-chip-on-PCB array of RNN accelerator.Our multi-die prototype performs LSTM inference with 192 hidden states in 330µs with a total system power of 9.0mW at 10MHz consuming 2.95µJ.Targeting the 8/16-bit quantization implemented in MUNTANIALA, we show a phoneme error rate (PER) drop of approximately 3% with respect to floating-point (FP) on a 3L-384NH-123NI LSTM network on the TIMIT dataset.
Application to visualize simultaneous vibrations of plates and ribs in a violin

A code was written to explore, in real-time, experimental operational deflection shapes up to 5 kHz of a violin soundbox. The animations can be rotated and zoomed using the mouse, while the developed graphical user interface (GUI) allows controlling the frequency, amplitude, and frame-per-second rate. Also, some parts of the model can be hidden to improve the visualization of specific details of interest. The measurements—performed by students as laboratory exercise—include the ribs and not only the plates as it is traditionally done, thus the vibrations of the soundbox can be analyzed as a whole. Since the GUI was developed under a cross-platform open-source software (scilab), it can be easily installed and used as an educational tool for traditional in-person learning or online learning.
An Analysis of a Blockchain-Enabled E-Government Document Interchange Architecture (DIA) in Thailand

In 2008, the world was introduced to Blockchain. In 2009, the first open-source Blockchainbased cyber currency was introduced as Bitcoin. By 2025, studies indicate that Blockchain-based enterprise-wide application revenue will reach nearly $20 billion. Therefore, this study proposes to build on the success of Thailand's National Single Window (THAINSW) gateway document interchange architecture (DIA) and further expand its availability to a much larger network of e-Government participants using Blockchain architecture. Therefore, the authors set how to investigate how and where Blockchain technology could be implemented in order to support of Thailand's 4.0 and digital economy visions. In addition to the qualitative analysis of the literature, the authors used input from a panel of 25 Thai experts to develop a foundation for the analysis based on Sathosi Nakamoto’s seven Blockchain disciplines. This analysis was enhanced from the authors’ own experiences in real-world Blockchain implementations. Results revealed that Blockchain is a highly innovative approach to secure transaction execution, information storage, performing functions, and establishing trust in an open architectural environment.Blockchain has the potential to increase good governance, increase transparency and privacy, and reduce the chance of corruption.
Cost‐effective multi‐instance multilabel active learning

Multi-instance multi-label (MIML) Active Learning (M2AL) aims to improve the learner while reducing the cost as much as possible by querying informative labels of complex bags composed of diverse instances. Existing M2AL solutions suffer high query costs for scrutinizing all relevant labels of MIML samples, querying excessive bag–label or instance–label pairs. To address these issues, a Cost-effective M2AL solution (CM2AL) is presented. CM2AL first selects the most informative bag–label pairs by leveraging uncertainty, label correlations, label space sparsity, and informativeness from queried instances of the bag, and thus avoids scrutinizing all labels. Next, it queries the most probably positive instance–label pairs of the selected bag–label pair. Particularly, if the feedback is positive, the bag is positively annotated with the label. For negative feedback, it further leverages the label of the neighborhood bags and the label of the nearby instances of queried instances of this bag, if the suggested labels from bag- and instance-levels disagree, CM2AL temporally gives up querying this bag–label pair and moves to another most informativeness one; otherwise, it takes the agreed label to annotate the bag, which further saves the cost by avoiding the excessive query. Extensive experiments on MIML data sets from diverse domains show that CM2AL can more reduce the cost while managing a better performance than state-of-the-art methods, the collaboration between bags and instances contributes to the saved cost.
Cash economy, criminality and cash regulation in Ethiopia

Purpose This paper aims to examine the contribution of Ethiopia’s cash economy to financial crimes. It also investigates the regulation of cash in the context of controlling crime stemming from the cash economy. Design/methodology/approach This study relies on primary data generated from 20 interviewees drawn from the National Bank of Ethiopia, Ethiopian Financial Intelligence Center, selected commercial banks and law enforcement agencies and document review from government reports, media press and statutes, as well as secondary data from online and offline sources. Findings The cash-intensive nature of Ethiopia’s economy has enabled a significant amount of cash to circulate outside of the formal financial system. This money is partly to blame for the prevalence of criminal activities such as cash hoarding, corruption and illicit financial flows. To address the threat of crime posed by the cash economy, the Ethiopian Government has taken measures such as restricting cash withdrawals from financial institutions, limiting the amount of cash individuals can hold and demonetizing the banknotes. The measures enable the banks to collect the cash circulating outside of the formal financial sector. However, the effect of these measures on reducing future criminality remains uncertain. Improving the financial inclusivity of the country, specifically expanding basic financial products to the rural areas, digitalizing the country’s payment system, raising general financial awareness and establishing a strong financial consumer protection framework would play a critical role in reducing future criminality and transforming the cash-intensive into a cashless economy. Originality/value This paper provides a first-of-its-kind analytical perspective on the contribution of Ethiopia’s cash economy to criminal activity and the adequacy of countermeasures so far taken.
Ethics, big social data, data sharing, and attitude among the millennial generation: A case of Thailand

Abstract Big social data and digital technologies create tremendous opportunities but raise questions and concerns on ethical data usage and sharing. Moreover, big social data plays a vital role in Thailand's 20‐year national strategy to turn Thailand into a developed nation by 2037, especially on security and human capital development strategies. Nonetheless, the progress in big social data must go hand‐in‐hand with ethical standards. To date, there are no universal ethical criteria for big social data sharing and governance. This study investigates the ethical issues of big data in social media. It maps big social data to workable ethical theories. The model of big social data sharing factors was proposed. Using Thailand as a case study, the exploratory study examined the digital behaviors and moral perceptions of the millennials' big social data sharing through 71 in‐depth interviews. The results revealed a strong pattern toward “ethical consequentialism” among the Thai millennials. Examining these findings fosters the formation of big social data ethics from the views of the data generators. This study has attempted to contribute to scholarship in the growing body of work on appropriate ethical guidelines for big social data sharing and help Thailand achieve its national strategy.
Fake Profile Detection and Stalking Prediction on Facebook

The increasing popularity and demand of social media has resulted in connecting people across the globe in a better way. The use of social media platforms to express their views and showcase their day-to-day life is increasing gradually. The activities related to social, business, entertainment and information are being exchanged regularly in social networking. In case of Facebook, there are approximately 1.5 billion users and this count is increasing daily. More than 10 million likes and shares are performed daily on Facebook. Many other networks, like LinkedIn, Instagram, Twitter, Snapchat, etc., are also growing exponentially. But, with all the advancements and growth, several problems are also introduced. Facebook has its own benefits to people but at the same time Facebook is being targeted for many malicious activities such as creating fake profiles to stalk people, online impersonation, etc., which can harm the reputation and invade privacy in online social platform. One of the challenging problems in social network security is to recognize the fake profiles. This has resulted in need of cybersecurity measures and applications to prevent people from cyberbullying such as stalking from fake profiles. In this paper, a framework to classify a Facebook profile as genuine or fake using machine learning techniques is proposed and the same framework will be used for the prediction of stalking.
Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Gujarati is the language used for everyday communication in the state of Gujarat, India.The Gujarati language is also officially recognized by the constitution and the government of India.Gujarati script is based on the Devanagari script.An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it.Idioms represent the cultural heritage of Gujarati language.Idioms are used in Gujarati language for effective communication and convey of an accurate message.No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language.Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom.Many forms of single idiom make automatic idiom identification as well as machine translation more challenging.This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language.This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text.The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%.The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.
Dynamic Node Allocation-Based Multirobot Path Planning

The previous multirobot path planning algorithm stipulated that more than two channels must be included in the map, causing the algorithm to malfunction in more demanding environments such as narrow passages or crossroads. In contrast, a path planning method based on heuristic search is proposed in this paper. The method truncates the path of robots and disables inverse edges so that it enables multirobots to run in a coordinated way under space-limited circumstances. First, real-time communication between the server and the robots is established. Then, the server uses heuristic search to plot a short path for the robot according to the obtained robot position and state information, during which the inverse edges of the node sequence allocated to other robots and the nodes occupied by stationary robots are not allowed to be visited. Finally, since the server has searched the paths for all the robots, the duplicated nodes are eliminated while the remnant nodes are sent to the corresponding robots. The above process loops until each robot reaches the target position. Experimental results show that this method improves the traffic efficiency of multirobots in narrow passages and allows robots to operate in an orderly manner.
Noise2Grad: Extract Image Noise to Denoise

In many image denoising tasks, the difficulty of collecting noisy/clean image pairs limits the application of supervised CNNs. We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. To ease the interference of the image background, we use a noise removal module to aid noise extraction. The noise removal module first roughly removes noise from the noisy image, which is equivalent to excluding much background information. A noise approximation module can therefore easily extract a new noise map from the removed noise to match the gradient of the noisy input. This noise map is added to a random clean image to synthesize a new data pair, which is then fed back to the noise removal module to correct the noise removal process. These two modules cooperate to extract noise finely. After convergence, the noise removal module can remove noise without damaging other background details, so we use it as our final denoising network. Experiments show that the denoising performance of the proposed method is competitive with other supervised CNNs.
Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization

Continuous DR-submodular maximization is an important machine learning problem, which covers numerous popular applications. With the emergence of large-scale distributed data, developing efficient algorithms for the continuous DR-submodular maximization, such as the decentralized Frank-Wolfe method, became an important challenge. However, existing decentralized Frank-Wolfe methods for this kind of problem have the sample complexity of $\mathcal{O}(1/\epsilon^3)$, incurring a large computational overhead. In this paper, we propose two novel sample efficient decentralized Frank-Wolfe methods to address this challenge. Our theoretical results demonstrate that the sample complexity of the two proposed methods is $\mathcal{O}(1/\epsilon^2)$, which is better than $\mathcal{O}(1/\epsilon^3)$ of the existing methods. As far as we know, this is the first published result achieving such a favorable sample complexity. Extensive experimental results confirm the effectiveness of the proposed methods.
Quantum control attack: Towards joint estimation of protocol and hardware loopholes

In this paper we present the quantum control attack on quantum key distribution systems. The cornerstone of the attack is that Eve can use unitary (polar) decomposition of her positive-operator valued measure elements, which allows her to realize the feed-forward operation (quantum control), change the states in the channel after her measurement and impose them to Bob. Below we consider the general eavesdropping strategy and the conditions those should be satisfied to provide the attack successfully. Moreover we consider several types of the attack, each of them is based on a different type of discrimination. We also provide the example on two non-orthogonal states and discuss different strategies in this case.