Reviewerly/exHarmony huggingface dataset:
Web Service-Based Turkish Automatic Speech Recognition Platform

In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture has emerged and gained a lot of popularity in recent years. Therefore, both for the use of microservices and for the provided of the necessary interface for Automatic Speech Recognition (ASR), a web-based platform has been developed. Within firstly the scope of the study, a Turkish ASR system was developed. A web service structure was created to facilitate access to the ASR system. The access of methods and data in the web service structure was provided through Representational State Transfer (REST) web services and service layer. An interface was developed to enable interaction with the web service. The platform was developed using a combination of different technologies such as ASR, web services, microservices, and interface technologies. The developed platform can be used via a standard web browser or an Application Programming Interface (API). In this study, Docker packages were used to improve system performance instead of using different virtual machines on a single server. In the experiments performed, it was shown that the Turkish ASR system had a word error rate of 24.70%. In web service performance tests, it was shown that the platform responded in an average of 9.6 seconds for a 59-second speech recording. The developed user interface was tested in both mobile and desktop web browsers and was shown to function properly. Applications and other services were given access to the platform without the need to use an interface via API support provided by the platform. As a result, a web service-based Turkish ASR platform working seamlessly on the ever-increasing number of mobile devices, the Internet of Things ecosystem, or other access devices was developed.
Paper Title Experimental Estimation of the Frequency Response of an Optical System Implementing the Phase Triangulation Method

The work is devoted to estimation of the amplitude frequency characteristics of the optical measuring system that implements method of phase triangulation. Method based on the analysis of images during spatial modulation of an optical source encoding a binary Gray code and on the spatial-frequency analysis of the image observed by the photo detector.
Secure IoT Device Architecture Using TrustZone

IoT realizes efficient system such as smart cities, smart factories, and smart agriculture. However, there are risks of cyber attacks against the IoT with the potential to cause serious damage. To protect the IoT systems, protection of the entire system including end-point IoT devices is essential. However, existing software-based protection is insufficient against recent sophisticated attackers who disable or bypass security mechanisms. In this paper, to ensure correct operations of security mechanisms, we propose a secure IoT device architecture using TrustZone. A monitoring engine can be protected from attacks by deploying the engine in a secure world which is isolated from a non-secure. Here, a problem is that each secure and non-secure world has its own virtual memory and OS, thus the monitoring engine in the secure world cannot directly monitor software in the non-secure world. To cope with the semantic gap between the non-secure world and the secure world, the proposed architecture has two monitoring engines: a monitoring engine in the non-secure world for measuring software in the non-secure world and a monitoring engine in secure world for attesting the engine in non-secure world. Moreover, we implement the architecture and show the proposed architecture is feasible on the basis of its evaluation results.
Microscale Precision of 6DOF Localization Rectification of Low-End Stereo Vision Using Deep Learning

This paper proposes the deployment of a deep feedforward neural network (DFF) for the estimation of an object's 6D pose with respect to a robot arm. DFF is also deployed as a data rectification stage. The proposed technique is able to achieve microscale precision using customized low-end stereo vision system, given the 3D location of each of the object's four bounding corners. The 4 bounding corners' 3D locations are estimated from the subpixel localization accuracy of each. The paper also proposes a building block for convolutional neural networks for object detection, which is based on utilization of skipping connections that concatenates early feature maps to later layers, instead of adding them like in Residual network (ResNet). The proposed building block shows better results than the building block of ResNet in terms of object detection accuracy. For minimizing the use of memory resources, this paper is recommending the use of batch normalization layer at the beginning of convolutional neural network architectures, empirically, it dispenses the need for data normalization.
Student Engagement in Improving Access to Taught Course Content at LSE Library: practicalities and Pitfalls

(2020). Student Engagement in Improving Access to Taught Course Content at LSE Library: practicalities and Pitfalls. New Review of Academic Librarianship: Vol. 26, Engaging students in taking academic libraries forward, pp. 403-418.
Performance2vec

When working on a new stochastic optimization algorithm, one task that should be performed is to compare its performance with those of state-of-the-art algorithms. The literature suggests that the most commonly applied approaches for comparing algorithms' performances use statistical analyses. However, to provide a more meaningful explanation about algorithms' performances, we propose a methodology, named performance2vec, which computes a vector representation of each algorithm's performance by embedding it in some performance space determined by a set of benchmark problems. Experimental results show that the proposed embeddings, paired with clustering approaches, provide a more in-depth explanation regarding the algorithms' performance by exploring the relations between them and the benchmark problems.
Effective image clustering using self-organizing migrating algorithm

Image segmentation is an important task in computer vision. Clustering is a common image segmentation approach which divides an image into homogeneous regions, but conventional clustering algorithms such as k-means have a tendency of getting stuck in local optima. In this paper, we propose a novel clustering algorithm based on the Self-Organizing Migrating Algorithm (SOMA). In particular, we adopt SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA, to image clustering. Experimental results on a set of benchmark images show excellent image clustering performance, also in comparison to other state-of-the-art metaheuristics.
Monte carlo tree search on perfect rectangle packing problem instances

We explore the possibilities of Monte Carlo tree search (MCTS), immensely successful in games such as Go and Chess, to solve the perfect rectangle packing problem. Experiments are done on two differently generated problem sets of 1,000 instances each, and we explore six different rollout numbers and two different action-selection strategies for MCTS. We compare the algorithm's performance to an exact depth-first algorithm equipped with efficient pruning techniques. By rating the number of solutions found against the total number of tiles placed, we define a 'computationally economic tradeoff'. Different rollout numbers and strategies lead to different results, both within and between the two problem sets. We discuss these results in context of other heuristic algorithms on this problem and closely related areas.
An Efficient Two-Level-Partitioning-Based Double Array and Its Parallelization

Trie is one of the most common data structures for string storage and retrieval. As a fast and efficient implementation of trie, double array (DA) can effectively compress strings to reduce storage spaces. However, this method suffers from the problem of low index construction efficiency. To address this problem, we design a two-level partition (TLP) framework in this paper. We first divide the dataset is into smaller lower-level partitions, and then we merge these partitions into bigger upper-level partitions using a min-heap based greedy merging algorithm (MH-GMerge). TLP has an excellent characteristic of load balancing and can be easily parallelized. We implemented two efficient parallel partitioned DAs based on TLP. Extensive experiments were carried out, and the results showed that the proposed methods can significantly improve the construction efficiency of DA and can achieve a better trade-off between construction and retrieval performance than the existing state-of-the-art methods.
Effective Deep-Learning-Based Depth Data Analysis on Low-Power Hardware for Supporting Elderly Care

We present a detailed technical insight into a commercial vision-based sensor for monitoring residents in elderly care facilities and alerting caretakers in case of dangerous situations such as falls or residents not returning to their beds during nighttime. We focus on aspects that enable deeplearning-based object classification in realtime on low-end ARM-based hardware, which is prerequisite for a solution that is performant yet affordable, low-power, and unobtrusive. To this end, we introduce an efficient vision pipeline that maps the input depth data to concise virtual top-views. These views are then processed by a set of convolutional neural networks, with a scheduler selecting the most appropriate one based on the current operating conditions and available hardware resources. In order to overcome the challenge of acquiring large amounts of training data in this privacy-critical environment, we pretrain these networks on a large set of synthetic depth data. These concepts are general and applicable to similar vision tasks.
Advances in Embodied Construction Grammar

Abstract This paper describes the continuing goals and present status of the ICSI/UC Berkeley efforts on Embodied Construction Grammar (ECG). ECG is semantics-based formalism grounded in cognitive linguistics. ECG is the most explicitly inter-disciplinary of the construction grammars with deep links to computation, neuroscience, and cognitive science. Work continues on core cognitive, computational, and linguistic issues, including aspects of the mind/body problem. Much of the recent emphasis has been on applications and on tools to facilitate new applications. Extensive documentation plus downloadable systems and grammars can be found at the ECG Homepage. 1
Development of Browser Extension for HTML Web Page Content Extraction

As the amount of content on the websites increases, automatic content extraction from Web pages becomes more important. Although many studies have been done in the literature on this subject, a method that fully solves the problem has not been revealed due to the flexible structure of HTML. The performances of the methods that show success at certain rates also decrease over time with the changing and developing Web structure. In this study, a browser extension was developed to automatically download text content on Web pages. This developed extension provides an output with 100% recall rate by cleaning the text content on the Web page from all tags and codes with a parser that utilizes the Document Object Model (DOM) structure. This browser extension that operates independently from the language has been tested on different types of popular Web sites in Turkey and has been shown to work successfully.
A Comparative Study of Text Classification and Missing Word Prediction Using BERT and ULMFiT

We perform a comparative study on the two types of emerging NLP models, ULMFiT and BERT. To gain insights on the suitability of these models to industry-relevant tasks, we use Text classification and Missing word prediction and emphasize how these two tasks can cover most of the prime industry use cases. We systematically frame the performance of the above two models by using selective metrics and train them with various configurations and inputs. This paper is intended to assist the industry researchers on the pros and cons of fine-tuning the industry data with these two pre-trained language models for obtaining the best possible state-of-the-art results.
Enhanced performance for the encrypted web through TLS resumption across hostnames

TLS can resume previous connections via abbreviated resumption handshakes that decrease the delay and save expensive cryptographic operations by reusing cryptographic TLS state from previous connections. TLS version 1.3 recommends avoiding resumption handshakes, when connecting to a different hostname. In this work, we reassess this recommendation, as we find that sharing cryptographic TLS state across hostnames is a common practice on the web. We propose a TLS extension that allows the server to inform the client about TLS state sharing with other hostnames. This information enables the client to efficiently resume TLS sessions across hostnames. Our evaluation indicates that our TLS extension provides performance gains for the web. For example, about 58.7% of the 20.24 full TLS handshakes that are required to retrieve an average website on the web can be converted to resumed connection establishments which reduces the CPU time consumed for TLS connection establishments by 44%. Furthermore, our TLS extension accelerates the connection establishment with an average website by up to 30.7%. Thus, our proposal significantly reduces the (energy) costs and the delay overhead in the encrypted web.
Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments

Cloud computing has become the most popular distributed computing paradigm among others which delivers scalable resources for efficient execution of large-scale scientific workflows. However, the large number of user requests and the limited cloud resources have posed a significant challenge on resource allocation, scheduling/mapping, power consumption, monetary cost, and so on. Therefore, how to schedule and optimize workflow execution in a cloud environment has become the most critical factor in improving the overall performance. Moreover, Multi-objective Optimization Problems (MOPs) along with heterogeneous cloud environments have made resource utilization and workflow scheduling even more challenging. In this work, we propose a novel algorithm, named Multi-objective Optimization for Makespan, Cost and Energy (MOMCE), to efficiently assign tasks to cloud resources in order to reduce total execution time, monetary cost, and energy consumption of scientific workflows. The experimental results have demonstrated the optimization stability and robustness of MOMCE algorithm for achieving a better fitness value in comparison with other existing algorithms.
On CDCL-Based Proof Systems with the Ordered Decision Strategy

We prove that CDCL SAT-solvers with the ordered decision strategy and the DECISION learning scheme are equivalent to ordered resolution. We also prove that, by replacing this learning scheme with its opposite, which learns the first possible non-conflict clause, they become equivalent to general resolution. In both results, we allow nondeterminism in the solver’s ability to perform unit propagation, conflict analysis, and restarts in a way that is similar to previous works in the literature. To aid the presentation of our results, and possibly future research, we define a model and language for CDCL-based proof systems – particularly those with nonstandard features – that allow for succinct and precise theorem statements.
LRZ Convolution: An Algorithm for Automatic Anomaly Detection in Time-series Data

Automatic anomaly detection is a hard but practically useful problem. With telemetry data sizes growing constantly, experts will rely increasingly on automation to bring anomalies to their attention. In this paper, anomaly transition points (called change points elsewhere), are determined using a novel application of a somewhat obscure statistical score called “z-score of mean difference”. Use of this score yields a practical linear-time algorithm called LRZ Convolution with sound statistical underpinnings, and which does not require data normality. Each anomaly transition point is accompanied by a set of explanatory predicates that can form a good starting point for determining an anomaly’s root causes. Careful experimental evaluation and performance in two independent domains show promising results. A preliminary comparison with a well-known machine learning algorithm called Support Vector Machines (SVM) yields a highly favorable outcome.
Real world morphological evolution is feasible

Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture the natural noise and richness of the real world. Very few of these virtual robots are built as physical robots, and the few that are will rarely be further improved in the actual environment they operate in, limiting the effectiveness of the automatic design process. We utilize our shape-shifting quadruped robot, which allows us to optimize the design in its real-world environment. The robot is able to change the length of its legs during operation, and is robust enough for complex experiments and tasks. We have co-evolved control and morphology in several different scenarios, and have seen that the algorithm is able to exploit the dynamic morphology solely through real-world experiments.
A quantum simulation algorithm for continuous optimization

This paper presents a novel multi-threaded quantum inspired optimization algorithm targeted at global search in continuous domains. The proposed approach is based on a Diffusion Monte Carlo (DMC) physical model and is characterized by a set of parallel quantum walk processes. The effectiveness of the proposed algorithm is demonstrated by experimental results on the 24 noiseless functions from the Black Box Optimization Benchmark of the Comparing Continuous Optimization benchmarking platform (COCO).
SLATE: Managing Heterogeneous Cloud Functions

This paper presents SLATE, a fully-managed, heterogeneous Function-as-a-Service (FaaS) system for deploying serverless functions onto heterogeneous cloud infrastructures. We extend the traditional homogeneous FaaS execution model to support heterogeneous functions, automating and abstracting runtime management of heterogeneous compute resources in order to improve cloud tenant accessibility to specialised, accelerator resources, such as FPGAs and GPUs. In particular, we focus on the mechanisms required for heterogeneous scaling of deployed function instances to guarantee latency objectives while minimising cost. We develop a simulator to validate and evaluate our approach, considering case-study functions in three application domains: machine learning, bio-informatics, and physics. We incorporate empirically derived performance models for each function implementation targeting a hardware platform with combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to homogeneous CPU and homogeneous FPGA functions, simulation results achieve respectively a cost improvement for non-uniform task traffic of up to 8.7 times and 1.7 times, while maintaining specified latency objectives.
Computer‐based evaluation to assess students' learning for the multiple‐choice question–based exams: CBE‐MCQs software tool

Abstract In this study, a computer‐based evaluation (CBE) software tool, namely CBE‐MCQs, is proposed to assist the instructor to evaluate and analyze traditional classroom‐based exams, which include multiple‐choice questions (MCQs) reliably, quickly, and automatically. Using the proposed CBE‐MCQs software tool while performing question analysis, the learning outcome‐based analyzes are performed by matching the learning outcomes of the courses with the exam questions. With question‐based evaluations, item analyzes such as item difficulty, item discrimination, and item distracters can be made easily. Also, with the question‐based evaluation, the instructor of the course can effectively measure the extent to which students achieve the learning outcomes of the related course. Thus, the CBE‐MCQs tool helps successful maintenance of both accreditation and training processes with the analysis it provides to the instructors. In addition, the final exam assessment of the course based on MCQs within the scope of the case study was carried out with the CBE‐MCQs software tool. Each MCQ was evaluated based on classical theory within the scope of difficulty index ( P ), discrimination index ( D ), and distracter efficiency ( DE ). It is observed that association between D and P was statistically significant according to Pearson correlation ( r = −0.600) and χ 2 ( χ 2 = 15.98, df = 8; α = 0.05). Also, a system usability survey of 10 statements was applied to the instructors using this software to evaluate the usability of the CBE‐MCQs software tool. As a result of a series of experimental studies and survey, the developed CBE‐MCQs software tool is evaluated as simple, effective, and satisfactory by 87.4% of the instructors.
Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations

This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space available. This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method.
TOSS: A Topology-based Scheduler for Storm C1usters

Nowadays, our world is undergoing profound challenges in processing a massive amount of data. A handful of computation technologies emerge as a promising computation platform for data intensive processing. Apache Storm is an outstanding open-source platform for largescale streaming computation, which is widely used in the industry (eg., Twitter). Performance bottleneck problems encountered in streaming data applications motivate us to investigate scheduling issues in Storm. A key aspect of tuning Storm performance is to decide how to deploy components of a storm application among all available nodes in a cluster. Driven by our observations, we design and implement a new scheduling strategy called TOSS based on application structures. Compared to the existing round-robin scheduler, TOSS not only judiciously handles tight-bind components, but also balances workloads by introducing a self-tuning mechanism in the deployment stage. We conduct experiments by applying two popular and distinct topologies to evaluate the performance of TOSS. The experimental results suggest that TOSS significantly boost the performance of the round-robin scheduler. ln particular, TOSS substantially improves the system throughput of Storm while shortens latency of Storm applications.