Heart valve disease has a large and growing burden, with a prognosis worse than many cancers. Screening with a traditional stethoscope is underutilised, often inaccurate even in skilled hands, and requires time-consuming, intimate examinations. Here, we present a handheld device to enable untrained users to record high-quality heart sounds without requiring patients to undress. The device incorporates multiple high-sensitivity sensors embedded in a flexible substrate, placed at key chest locations by the user. To address challenges from localised heart sound vibrations and noise interference, we developed time-frequency signal quality algorithms that automatically select the best sensor in the device and reject recordings with insufficient diagnostic quality. A validation study demonstrates the device's effectiveness across a diverse range of body types, with multiple sensors significantly increasing the likelihood of a successful recording. The device has the potential to enable accurate, accessible, low-cost heart disease screening.

This paper presents a platform for measuring, evaluating and modeling the energy efficiency aspects of an O-RAN 5G wireless network. We describe our open-source based O-RAN testbed which includes both bare-metal and Kubernetes network functions, in addition to physical network components. We focus on measuring power consumption of servers and workloads of cloudified and virtualized network functions. We show that a combination of different power measurements can be used successfully to achieve the accuracy and granularity required for energy efficiency measurement, evaluation and modeling.

Dynamic cache partitioning for shared Last Level Caches (LLC) is deployed in most modern multicore systems to achieve process isolation and fairness among the applications and avoid security threats. Since LLC has visibility of all cache blocks requested by several applications running on a multicore system, a malicious application can potentially threaten the system that can leverage the dynamic partitioning schemes applied to the LLCs by creating a timing-based covert channel attack. We call it as Cache Partitioned Covert Channel (CPCC) attack. The malicious applications may contain a trojan and a spy and use the underlying shared memory to create the attack. Through this attack, secret pieces of information like encryption keys or any secret information can be transmitted between the intended parties. We have observed that CPCC can target single or multiple cache sets to achieve a higher transmission rate with a maximum error rate of 5% only. The paper also addresses a few defense strategies that can avoid such cache partitioning based covert channel attacks.

Finding ways to help users assess relevance when they search using math expressions is critical for making Mathematical Information Retrieval (MIR) systems easier to use. We designed a study where participants completed search tasks involving mathematical expressions using two different summary styles, and measured response time and relevance assessment accuracy. The control summary style used Google's regular hit formatting where expressions are presented as text (e.g. in LaTeX), while the second summary style renders the math expressions. Participants were undergraduate and graduate students. Participants in the rendered summary style (n=19) had on average a 17.18% higher assessment accuracy than those in the non-rendered summary style (n=19), with no significant difference in response times. Participants in the rendered condition reported having fewer problems reading hits than participants in the control condition. This suggests that users will benefit from search engines that properly render math expressions in their hit summaries.

To use math expressions in search, current search engines require knowing expression names or using a structure editor or string encoding (e.g., LaTeX). For mathematical non-experts, this can lead to an "intention gap" between the query they wish to express and what the interface will allow them to express. min is a search interface that supports drawing expressions on a canvas using mouse/touch, keyboard and images. We present a user study examining whether min changes search behavior for mathematical non-experts, and to identify real-world usage scenarios for multimodal math search interfaces. Participants found query-by-expression using hand-drawn input useful, and identified scenarios in which they would like to use systems like min such as for locating, editing and sharing complex expressions (e.g., with many Greek letters), and working on complex math problems.

For the task-scheduling problem, we propose an augmented neural-network approach, which allows the integration of greedy as well as nongreedy heuristics (AugNN-GNG), to give improved solutions in a small number of iterations. The problem we address is that of minimizing the makespan of n tasks on m identical machines (or processors), where tasks are nonpreemptive and follow a precedence order. The proposed approach exploits the observation that a nongreedy search heuristic often finds better solutions than do their greedy counterparts. We hypothesize that combinations of nongreedy and greedy heuristics when integrated with an augmented neural-network approach can lead to better solutions than can either one alone. We show the formulation of such integration and provide empirical results on over a thousand problems. This approach is found to be very robust in that the results were not very sensitive to the type of greedy heuristic chosen. The new approach is able to find solutions, on average, within 1.8% to 2.8% of the lower bound compared to 2.0% to 8.3% for the greedy-only AugNN approach. This improvement is obtained without any increase in computational complexity. In fact the number of iterations used to find the solution decreased.

In a concurrent system with N processes, vector clocks of size N are used for tracking dependencies between the events. Using vectors of size N leads to scalability problems. Moreover, association of components with processes makes vector clocks cumbersome and inefficient for systems with a dynamic number of processes. We present a class of logical clock algorithms, called chain clock, for tracking dependencies between relevant events based on generalizing a process to any chain in the computation poset. Chain clocks are generally able to track dependencies using fewer than N components and also adapt automatically to systems with dynamic number of processes. We compared the performance of Dynamic Chain Clock (DCC) with vector clock for multithreaded programs in Java. With 1% of total events being relevant events, DCC requires 10 times fewer components than vector clock and the timestamp traces are smaller by a factor of 100. Although DCC requires shared data structures, it is still 10 times faster than vector clock in our experiments.