Reviewerly/exHarmony huggingface dataset:
Auditable Federated Learning With Byzantine Robustness

Machine learning (ML) has led to disruptive innovations in many fields, such as medical diagnoses. A key enabler for ML is large training data, but existing data, such as medical data, are not fully exploited by ML because of data silos and privacy concerns. Federated learning (FL) is a promising distributed learning paradigm to address this problem. On the other hand, existing FL approaches are vulnerable to poisoning attacks or privacy leakage from a malicious aggregator or client. This article proposes an auditable FL scheme with Byzantine robustness against the aggregator and client: The aggregator is malicious but available, and the client could perform poisoning attacks. First, the Pedersen commitment scheme (PCS) for homomorphic encryption was applied to preserve privacy and for commitments to the FL process to achieve auditability. The auditability enables clients to verify the correctness and consistency of the entire FL process and to identify parties that misbehave. Second, an efficient technique of divide and conquer was designed based on PCS to allow parties to cooperate and securely aggregate gradients to defend against poisoning attacks. This technique enables clients to share no common secret key and cooperate to decrypt ciphertext, guaranteeing a client’s privacy even if some other clients are corrupted by adversaries. This technique was optimized to tolerate the dropout of clients. This article reports a formal analysis concerning privacy, efficiency, and auditability against malicious participants. Extensive experiments on various benchmark datasets show that the scheme is robust with high model accuracy against poisoning attacks.
Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Arbitrary moving object detection including vehicles and human beings in the real environment, such as protected and sensitive areas, is challenging due to the arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The proposed method segments foreground objects from the background using the frame difference between the median frame and individual frames. This step outputs several different foreground information. The mean of foreground images is computed, which is referred to as the mean activation map. For the mean activation map, the method employs the fast Fourier transform, which outputs amplitude and frequencies. The mean of frequencies is computed for moving objects in using activation maps of temporal frames, which is considered as a frequency feature vector. The features are normalized to avoid the problems of imbalanced features and class sizes. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of arbitrary moving and static videos. For evaluating the proposed method, we construct our dataset, which contains videos of static and arbitrarily moving objects caused by shaky cameras and wind. The results of the video dataset show that the proposed method achieves the state-of-the-art performance.
Experience is the Best Teacher: Personalized Vocabulary Building Within the Context of Instagram Posts and Sentences from GPT-3

Although language learners have different contexts and motivations, sensing personal backgrounds to optimize learning materials has still been challenging. By focusing on the huge movement of Social Networking Services (SNS) such as Instagram, we came up with the idea of utilizing social posts, in particular images, as learning materials. This paper presents our working prototype of the proposed system that extracts keywords from these images and leverages GPT-3 to generate sentences for acquiring new vocabulary around the keywords. By conducting a pilot study involving three users, we found that 2.2 words appeared as unknown words for the user in one generated sentence on average, and there is room for improvement in the proposed system. These findings can be utilized in a large-scale evaluation designed in the future.
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense Network

For years, Single Image Super Resolution (SISR) has been an interesting and ill-posed problem in computer vision. The traditional super-resolution (SR) imaging approaches in- volve interpolation, reconstruction, and learning-based methods. Interpolation methods are fast and uncomplicated to compute, but they are not so accurate and reliable. Reconstruction-based methods are better compared with interpolation methods, but they are time-consuming and the quality degrades as the scaling increases. Even though learning-based methods like Markov random chains are far better than all the previous ones, they are unable to match the performance of deep learning models for SISR. This study examines the Residual Dense Networks architec- ture proposed by Yhang et al. [17] and analyzes the importance of its components. By leveraging hierarchical features from original low-resolution (LR) images, this architecture achieves superior performance, with a network structure comprising four main blocks, including the residual dense block (RDB) as the core. Through investigations of each block and analyses using various loss metrics, the study evaluates the effectiveness of the architecture and compares it to other state-of-the-art models that differ in both architecture and components.
Differences in the perception of village learning curriculum between general and innovative school teachers

The purpose of this study is to analyze the difference in perception of village learning curriculum between teachers of general schools and innovative schools. To this end, a survey was conducted on a total of 802 elementary and middle school teachers in B area, and the necessity, operation principle, content, and method of village learning curriculum recognized by general and innovative school teachers were analyzed through reliability analysis, frequency analysis, average analysis, cross analysis, and t verification. The results are as follows. First, there was a statistically significant difference between general schools and innovative schools in all questions about the necessity and expected effect of the village learning curriculum. Second, in the operating principle of the village learning curriculum, all nine questions showed statistically significant differences between general schools and innovative schools. Third, there were statistically significant differences between general schools and innovative schools in all questions about the content and method of the village learning curriculum. Lastly, there were no statistically significant differences in the remaining five questions except for the lack of interest and understanding of teachers, lack of support from school managers, opposition from parents, and lack of available facilities. Based on these results, discussions and conclusions were presented focusing on ways to improve the village learning curriculum derived.
Teacher's Perspective towards EFL Students' Speaking Performance in Post Covid-19 Pandemic Era

When learning English, speaking is one of the English skills which is important to learn. Learning to speak will make students able to communicate with others and know what happens in the world. In fact, learning to speak is more challenging than learning other skills in English. In order to speak effectively, students need to practice their speaking regularly. However, the appearance of Covid-19 limits students to practice their speaking face-to-face. After teachers and students adapt to online learning for 2 years, suddenly learning process back to offline learning with certain regulations. Due to that problem, this descriptive qualitative study came up intending to explore the perspective of the teacher regarding EFL students’ speaking performance in the pandemic context and the factors affecting it. Using semi-structured interviews as the instrument, an English teacher who teaches English pre, during, and post-pandemic in SMA Widya Gama Malang has been interviewed in this research. The results of this study revealed that reading material has an impact on students’ speaking performance, the difference in students’ self-confidence during and after the pandemic, students’ willingness to speak during and after the pandemic from the teacher’s perspective, ICT is the best multimedia factor for improving speaking performance, and there is no fixed teaching methodology for teaching speaking.
On the detection of lateral movement through supervised machine learning and an open-source tool to create turnkey datasets from Sysmon logs

Abstract Lateral movement (LM) is a principal, increasingly common, tactic in the arsenal of advanced persistent threat (APT) groups and other less or more powerful threat actors. It concerns techniques that enable a cyberattacker, after establishing foothold, to maintain ongoing access and penetrate further into a network in quest of prized booty. This is done by moving through the infiltrated network and gaining elevated privileges using an assortment of tools. Concentrating on the MS Windows platform, this work provides the first to our knowledge holistic methodology supported by an abundance of experimental results towards the identification of LM via machine learning (ML) techniques. We not only assess this potential by means of both traditional and deep learning models, but also contribute a publicly available, open-source tool, which can convert Windows system monitor logs to turnkey datasets, ready to be fed into ML models. Vis-`a-vis the relevant literature, and by considering a highly unblalanced dataset and a multiclass classification problem, we report superior scores in terms of the F1 and AUC metrics, 99.41% and 99.84%, respectively.
Three Predictions of Gravitational Waves, and Black Holes

Gravitational wave forms a focus of physics, astrophysics and cosmology. First, we propose three predictions of gravitational waves: their observations must be nonlinear waves; velocity of gravitational wave should be slightly higher than the velocity of light; gravitational waves in black holes may emit and be observed. Next, we discuss the analogies of the gravitational and electromagnetic fields, and research some relations between black holes and gravitational waves. Finally, we propose the directed gravitational wave observatories of high-energy astrophysics, as such for the huge black holes in the Galactic center.
Job Recommendation System for Daily Paid Workers using Machine Learning

Abstract: In developing countries where unskilled workers often face challenges in finding suitable employment, this is where recommendation systems come in, as they can help users find information that is specific to their interests. Using the system decision-making and predictions through algorithms trained on available data across multiple domains makes it easy for users to access pertinent information. One area where recommendation systems can have a significant impact is in helping unskilled workers find jobs based on their skills and interests. Although there are many jobs available for skilled professionals, it can be challenging for daily wage workers to find suitable employment due to a lack of information and awareness. Currently, there are no relevant recommendation systems available to help these workers. In this research, we propose a "Job recommendation system for daily paid workers using Machine Learning" that analyzes a worker’s skills and interests to find appropriate job opportunities. To ensure that the system is robust, we consider a wide range of factors when recommending jobs to daily wage workers.
Calculating lexicase selection probabilities is NP-Hard

Lexicase selection is a state-of-the-art parent selection algorithm in evolutionary computation. Recently, there have been efforts to develop stronger theoretical analyses of lexicase selection. Many of these analysis hinge on calculating the probability of an individual solution being selected under lexicase selection. Discovering a fast way to perform this calculation would be very helpful to the development of theory regarding lexicase selection, and would also have implications for efforts to develop practical improvements to lexicase selection. Here, I prove that this problem of calculating selection probabilities under lexicase selection, which I name lex-prob, is NP-Hard. I achieve this proof by reducing SAT, a well-known NP-Hard problem, to lex-prob in polynomial time. This reduction involves an intermediate step in which a popular variant of lexicase selection, ∊-lexicase selection, is reduced to standard lexicase selection. This result also has deeper theoretical implications about the relationship between ∊-lexicase selection and lexicase selection and the relationship between lex-prob and other NP-Hard problems. Finally, I present a highly-optimized brute-force algorithm (and open-source implementation) for performing these calculations. While the worst-case time complexity of this solution is, of course, exponential it is capable of solving realistically-sized instances of the problem in approximately 15 seconds.
Stochastic Soiling Loss Models for Heliostats in Concentrating Solar Power Plants

Reflectance losses on solar mirrors due to soiling are a significant challenge for Concentrating Solar Power (CSP) plants. Soiling losses can vary significantly from site to site -- with (absolute) reflectance losses varying from fractions of a percentage point up to several percentage points per day (pp/day), a fact that has motivated several studies in soiling predictive modelling. Yet, existing studies have so far neglected the characterization of statistical uncertainty in their parameters and predictions. In this paper, two reflectance loss models are proposed that model uncertainty: an extension of a previously developed physical model and a simplified model. A novel uncertainty characterization enables Maximum Likelihood Estimation techniques for parameter estimation for both models, and permits the estimation of parameter (and prediction) confidence intervals. The models are applied to data from ten soiling campaigns conducted at three Australian sites (Brisbane, Mount Isa, Wodonga). The simplified model produces high-quality predictions of soiling losses on novel data, while the semi-physical model performance is mixed. The statistical distributions of daily losses were estimated for different dust loadings. Under median conditions, the daily soiling losses for Brisbane, Mount Isa, and Wodonga are estimated as $0.53 \pm 0.66$, $0.08 \pm 0.08$, and $0.58 \pm 0.15$ pp/day, respectively. Yet, higher observed dust loadings can drive average losses as high as $2$ pp/day. Overall, the results suggest a relatively simple approach characterizing the statistical distributions of soiling losses using airborne dust measurements and short reflectance monitoring campaigns.
Optimal Layout Synthesis for Quantum Circuits as Classical Planning (full version)

In Layout Synthesis, the logical qubits of a quantum circuit are mapped to the physical qubits of a given quantum hardware platform, taking into account the connectivity of physical qubits. This involves inserting SWAP gates before an operation is applied on distant qubits. Optimal Layout Synthesis is crucial for practical Quantum Computing on current error-prone hardware: Minimizing the number of SWAP gates directly mitigates the error rates when running quantum circuits. In recent years, several approaches have been proposed for minimizing the required SWAP insertions. The proposed exact approaches can only scale to a small number of qubits. Proving that a number of swap insertions is optimal is much harder than producing near optimal mappings. In this paper, we provide two encodings for Optimal Layout Synthesis as a classical planning problem. We use optimal classical planners to synthesize the optimal layout for a standard set of benchmarks. Our results show the scalability of our approach compared to previous leading approaches. We can optimally map circuits with 9 qubits onto a 14 qubit platform, which could not be handled before by exact methods.
Labor interests as a key factor in the involvement and performance of educational technology employees

Background. Work activity in a post-industrial digital society diYers fundamentally from our past ideas about work. Ze characteristics of this activity are changing just as rapidly, the motivation in employees of the information technology sphere, in particular. In this regard, the study of the role of labor interests, their relationship with the involvement and e[ciency of workers in this area remains an urgent problem. Objective. Ze research analyses the relationship between labor interests as a value-motivational, emotionally colored position of an employee, expressed in a practical attitude to work, and the organizational and job involvement and employee’s performance. Methods. To measure the level of employees’ involvement and their interest, an author’s questionnaire of 54 questions was compiled, including the scales of work and organizational involvement and the measurement of labor interests. Also, evaluation sheets to measure performance were developed in collaboration with the heads of the company in question. Zree groups of criteria were covered: qualitative, quantitative and temporal. Sample. Ze study involved personnel of an international company operating in the \eld of educational technologies in Russian and European markets. Ze survey involved respondents of various job levels from top management to specialists (N = 155, 48% women, 52% men), aged 18 to 36 years with work experience from 0.5 to 3 years. 60% of respondents have higher education, 40% of participants combine work and studies, reporting incomplete higher education. Results. Ze study results were veri\ed with the Spearman correlation analysis. Factors of organizational and work involvement, types of labor interests, as well as performance were subjected to veri\cation. Signi\cant correlations were found on almost all scales, which con\rmed the hypothesis that posited involvement of employees as being directly dependent on their interests. Career interests were signi\cantly associated with the qualitative criterion of performance at the level of p &lt; 0.01. Referential interests also had a correlation with employees’ performance. It turned out that top managers of the company have the highest performance and involvement rates. Conclusion. From the obtained correlation of professional interests and work involvement, it follows that a person is more actively immersed in work when the professional interests are taken into account. Ze connection between corporate interests and organizational involvement was also con\rmed. It was found that organizational and job involvement are associated with such spheres of employees’ interest as group, career, professional, corporate and referential.
Deep Learning-based Viewpoint Prediction Model and Influencing Attention Factors for Design Drawings

In order to meet the requirement of fine-grained viewpoint prediction for design drawings, a deep learning-based viewpoint prediction model is developed to achieve heat map generation, feature element recognition and interactive and instantaneous detection of design solutions, and the attentional influencing factors are discussed. A saliency map is introduced to model the visual attention allocation mechanism, and a fully convolutional networks-based Image Viewpoint Prediction Model (IVPM) is proposed to overcome the limitations of eye tracker tests. The model has excellent temporal performance after training on the Graphics Design Importance (GDI) dataset, and experiments validate that low-level attributes of images are the main influences on design attention. The IVPM can be applied to image creation, poster design, packaging design, product design and interface design, and is a useful reference for the creative design field.
Cybercrime

This contribution discusses the challenges that cybercrime poses for law enforcement and the criminal justice system. Cybercrime, as a side effect of digital innovation, acts as a disruptor of the existing models of crime prevention and criminal justice. In this sense, a redesign of the social reaction systems is expected. From a crime prevention perspective, the volume of cyber-offences has pushed through the adoption of technical measures (e.g., antivirus) developed by private entities while the public perception of law enforcement's role as a major actor in cybercrime prevention has remained weak. Moreover, from an investigative perspective, private institutions have regained their momentum while the public response has been reorganising around new centres of expertise to cope with the lack of digital evidence. These disruptive effects have also impacted trials so that, increasingly, legal professionals, as in cyber investigations, need to be capable of understanding the challenges and limits of digital evidence.
Argument placement in Icelandic

This paper gives an overview of the Icelandic data in the Nordic Word Order Database (NWD; Lundquist et al. 2019). The data were collected from 30 native speakers of Icelandic, and the experimental task elicited argument placement (subject shift, object shift, long object shift, particle shift). The results confirm that subject shift of definite subjects and pronouns is produced categorically in Icelandic, as is pronominal object shift. With regard to the placement of non-pronominal objects relative sentence adverbials and particles, there is, on the other hand, variability. Rather surprisingly, the results suggest that the placement of subjects (initial or postverbal) influences non-pronominal object shift. With respect to the ordering of objects and particles, the distinction between directional and metaphorical particles is relevant. The patterns are partly different from what we find in the comparable data from the other North Germanic languages (also available in NWD).